Zhifei Zhang
email: zhifei.zhang@mail.ecust.edu.cn

Zhihao Ling
email: zhhling@ecust.edu.cn

Alain Sarlette
email: alain.sarlette@inria.fr

Modified Integral Control Globally Counters Symmetry-Breaking Biases

Keywords: geometric control theory, Lie groups, symmetry breaking, coordinated swarms

We propose a modified integral controller to treat actuation biases in nonholonomic systems. In such systems, although one cannot directly counter biases along all directions, one can still aim at restoring the symmetry of coordinated motion that the biases would break. Focusing on the example of steering controlled planar vehicles, we indeed show how to perfectly stabilize a circular trajectory coordinated with the leader, despite any unknown actuation bias. The proposed solution is a simple modified integral control term, and we prove via a detailed analysis of averaging theory that this control gadget globally stabilizes the perfect coordinated motion. The corresponding general observation is that stabilizing such symmetry-preserving situation can be viewed as rejecting a bias affecting the output measurement, instead of the input command. The symmetry-restoring control then becomes an output bias rejection, dual to the more standard input bias rejection with standard integral control.

Introduction

Coordinated motion of controlled vehicles in robotics has drawn great attention during the last two decades [START_REF] Bai | Rigid body attitude coordination without inertial frame information[END_REF][START_REF] Sarlette | Autonomous rigid body attitude synchronization[END_REF][START_REF] Leonard | Collective motion, sensor networks and ocean sampling[END_REF][START_REF] Justh | Equilibria and steering laws for planar formations[END_REF][START_REF] Sepulchre | Stabilization of planar collective motion: All-to-all communication[END_REF]. A basic control primitive is to design local interactions among vehicles, such that their emerging behavior corresponds to moving like a single rigid body. This stabilizes a manifold of states corresponding to the symmetry of invariant relative configuration, and leaves open how one might further drive the swarm along this manifold, towards specific relative configurations or a specific motion of the center of mass [START_REF] Leonard | Collective motion, sensor networks and ocean sampling[END_REF][START_REF] Sepulchre | Stabilization of planar collective motion: All-to-all communication[END_REF]. The most notable examples are steering-controlled vehicles (see same references). Control laws and performance proofs have been established, assuming that all the vehicles have exactly equal dynamics. However, real vehicles will not satisfy this symmetry under permutation perfectly. Part of this modeling error will be countered purposefully by the coordination controller, avoiding vehicles to drift completely apart over long times. However, small modeling errors that break the permutation symmetry will typically, also in the presence of control, break the target symmetry of coordinated motion: due to small residual velocity errors, vehicles will slowly but persistently drift with respect to each other (see simulations in Section 4 for an illustration). Depending on the application context, such unexpected symmetry breaking could be truly detrimental and it appears meaningful to search for a simple fix of the situation. The improved controller might also serve in other applications like information protection codes, where the very purpose is to isolate thanks to coordination mechanisms, a symmetric manifold on which realistic perturbations imply a minimal drift.

When a system is subject to time-independent biases, a control engineering standard is to write a dynamical observer algorithm, which estimates the values of the bias parameters from all the known signals-i.e., input commands and output measurements. A well-designed estimator should asymptotically converge towards the true bias values, and those can then be explicitly compensated for in the input commands. The canonical way of writing such an estimator builds on Bayes reasoning, using a full copy, inside the algorithm, of the system to be controlled. However, relying on the stabilization provided by feedback control, less accurate and much simpler bias-compensating schemes are also viable. One such approach, which we pursue in the present paper, is integral control [START_REF] Aström | Feedback Systems: an Introduction for Scientists and Engineers[END_REF]. This concept just adds to the control input, a term proportional to the integral of the measured error. Under a constant nonzero error, the integral would keep increasing, so if the system stabilizes to a constant value, this must correspond to zero error. A system analysis is then needed to prove that the system indeed converges, but usually this can be established under quite general properties without requiring detailed system knowledge.

The idea in the present paper is thus to apply this integral control strategy in order to restore perfect symmetry in coordinated motion, despite possible biases that make the subsystems different from each other. This application features two particular challenges:

1.

The state space and therefore the error space is not a linear vector space, but a nonlinear manifold, more precisely a compact Lie group. This requires to revisit the concept of "integrating the error", since summing up or integrating variables relies on a vector space structure. The solution in [START_REF] Zhang | Integral control on Lie groups[END_REF][START_REF] Maithripala | An intrinsic PID controller for mechanical systems on Lie groups[END_REF] proposes to integrate instead the error-proportional feedback action, brought back to the Lie algebra (which is a vector space) thanks to standard Lie group operations. This error-proportional feedback is the main controller in standard studies without bias, like [START_REF] Bai | Rigid body attitude coordination without inertial frame information[END_REF][START_REF] Justh | Equilibria and steering laws for planar formations[END_REF][START_REF] Sepulchre | Stabilization of planar collective motion: All-to-all communication[END_REF], and we thus only suggest to add a small multiple of its integral. The idea is that, similarly, if an error-proportional feedback action constantly fights against the same bias, then the integral would keep increasing; so if the controlled system stabilizes it must be at a situation with zero error-proportional feedback action, hence in principle (under some conditions, and at least locally) with zero error. By assuming full actuation, Refs. [START_REF] Zhang | Integral control on Lie groups[END_REF][START_REF] Maithripala | An intrinsic PID controller for mechanical systems on Lie groups[END_REF] prove that any constant bias on a Lie group can be countered in this way.

2.

A new challenge in the present paper is the nonholonomic actuation typical of such systems, also sometimes called underactuation. This means that, if a bias pushes the system with some vector v, it is not necessarily physically possible with our actuator to push in the direction -v even if we knew it. Think for instance of a ship subject to lateral drift, or of a steering-controlled vehicle with translation velocity fixed at a wrong value. In standard linear control, this situation can appear as well. Consider ẋ = Ax + Bu + Fv with perturbation force Fv that lies outside the range of actuation B u; then, there is no way to stabilize x = 0 exactly, since this simply cannot be made a steady state. In the coordination application, the goal is to stabilize a manifold of relative equilibria corresponding to any configuration compatible with synchronized right-invariant Lie group velocities [START_REF] Hanssmann | Symmetry and Reduction for Coordinated Rigid Bodies[END_REF][START_REF] Sarlette | Coordinated motion design on Lie groups[END_REF]. Configurations where the vehicles can keep moving in a coordinated way appear feasible even for nonholonomic systems with uncountered bias.

We here propose a simple modified integral control gadget, which we call moderating integral control (MIC) as it acts by reducing the error-proportional output feedback in a suitable way, and which indeed stabilizes coordinated motion perfectly, despite actuation biases and for underactuated systems. In the present paper, we develop this idea in detail for steering controlled planar vehicles. We prove that our MIC not only restores symmetry exactly, but it ensures global convergence to this situation from any initial condition, like in the absence of bias the original error-proportional feedback controller did. It thus appears like a plug-and-play adaptation which could open up discussion for a more general use of integral control as a mechanism in symmetry-restoring applications.

As an independent observation, we note that the MIC is a suitable algorithm integrating accurate inputs to reject bias in outputs; this is dual to the usual integral control integrating accurate outputs to reject bias in inputs.

The paper is organized as follows:

• In Section 2, we describe the abstract Lie group model for stabilizing an underactuated rigid body in a fixed relative configuration with respect to a swarm leader, which is thus the geometric invariant related to the symmetry of rigid body motions. While Refs. [START_REF] Justh | Equilibria and steering laws for planar formations[END_REF][START_REF] Sepulchre | Stabilization of planar collective motion: All-to-all communication[END_REF][START_REF] Sarlette | Coordinated motion design on Lie groups[END_REF], among others, have derived corresponding controllers for perfectly modeled vehicles, we insist on achieving the same performance when the controlled vehicle is subject to biases or calibration errors on its translation velocity, both on actuated and on unactuated directions of motion. We progressively explain why coordinated motion/restoring symmetry is a proper objective in the presence of bias. We end this section by proposing a moderating integral controller (MIC) to solve this task.

•

In Section 3, we carry out a detailed convergence analysis for stabilizing the steering controlled vehicle into a given circular motion ( [START_REF] Justh | Equilibria and steering laws for planar formations[END_REF], see [START_REF] Leonard | Collective motion, sensor networks and ocean sampling[END_REF] for a concrete application), despite the actuation biases. We start the section by a more concrete formulation for the steering controlled vehicle in the plane. The main result proves how our MIC allows for still ensuring global convergence towards exact coordinated motion. The result combines a Lyapunov function adapted from the bias-less case [START_REF] Sepulchre | Stabilization of planar collective motion: All-to-all communication[END_REF][START_REF] Sarlette | Coordinated motion design on Lie groups[END_REF], with a detailed analysis of averaging theory along the lines of [START_REF] Aeyels | On exponential stability of nonlinear time-varying differential equations[END_REF], and showing that the averaging approximation becomes exact as we approach the target.

•

Section 4 provides simulation results illustrating the working of our controller for the steering controlled vehicle and giving a hint at expected performance.

•

In Section 5, we note how the MIC can be reformulated in an abstract context of output biases, to possibly solve problems beyond symmetry-preservation. In particular, we discuss its application to linear systems, where it enables a trade-off between rejecting biases on the input or on the output.

General Problem Setting and Controller Definition

Underactuated Rigid Body Motion

The motion of a rigid body can be described on a Lie group by

d dt g = g ξ,
where g is the position on the Lie group G and ξ is the Lie velocity in body frame; the latter belongs to the Lie algebra g, i.e., the tangent vector space to the Lie group at identity. Examples of this model include rigid body attitude control, where g is a rotation matrix and ξ is an angular velocity matrix [START_REF] Sarlette | Autonomous rigid body attitude synchronization[END_REF]; or the motion of a steering controlled vehicle [START_REF] Justh | Equilibria and steering laws for planar formations[END_REF], where

g =    cos θ sin θ p x -sin θ cos θ p y 0 0 1    , ξ =    0 ω v -ω 0 0 0 0 0    , (1)
with p x , p y the vehicle position, θ its heading, v a fixed translation velocity in body frame and ω the controlled angular rotation rate. We will treat this last example concretely and in detail.

For controlling satellite orientation, one can usually apply any angular velocity in g = so(3) (full actuation [START_REF] Sarlette | Autonomous rigid body attitude synchronization[END_REF]). In contrast, a vehicle can usually not have a sidewards velocity; in model [START_REF] Bai | Rigid body attitude coordination without inertial frame information[END_REF], we can command one single degree of freedom in ξ, which is thus restricted to a subset of g = se(2). This situation is called underactuation or nonholonomic motion. While a nonholonomic system may still reach all g ∈ G, i.e., for the vehicle all the three-dimensional values of heading θ and position p x , p y , it can only do so indirectly, and this complicates bias rejection.

Nonholonomic systems are standard in geometric control and the stabilization of symmetries (see the next paragraph). However, surprisingly little has been said about what happens to symmetries when the actual vehicle model differs from the nominal one. We therefore introduce an actuation bias explicitly into the model:

ξ = A + u + u B ,
where A ∈ g is a fixed drift velocity, the control u belongs to a subspace U ⊂ g orthogonal to A, and u B ∈ g is an actuation bias.

When u B lies in the same subspace U as u, countering the bias comes down to knowing its value and applying an opposite correction, u = -u B + u 0 where u 0 ∈ U would be the control input applied in the absence of bias. One approach is to write a so-called dynamical observer [START_REF] Bonnabel | Symmetry-preserving observers[END_REF][START_REF] Lageman | Gradient-like observers for invariant dynamics on a Lie group[END_REF][START_REF] Khosravian | Observers for invariant systems on Lie groups with biased input measurements and homogeneous outputs[END_REF][START_REF] Aguiar | Trajectory-tracking and path-following of underactuated autonomous vehicles with parametric modeling uncertainty[END_REF], which estimates all the variables that have known dynamical models, including the constant biases. A computationally less intensive approach would use integral control [START_REF] Aström | Feedback Systems: an Introduction for Scientists and Engineers[END_REF]. If the nominal input is u = u 0 and the measured deviation from target at time t is e(t), then standard integral control applies u = u 0k I u I with

d dt u I (t) = e(t) . (2)
Thus, "the longer the error has been positive, the stronger we consider pushing in the negative direction". Mathematically, if u I stabilizes to a fixed value, then Equation ( 2) implies e(t) = 0. The computation (2) only makes sense if u I and e belong to the same vector space. Thus, if for instance u I is a Lie group velocity and e a Lie group configuration (error) as in (1), it must be adapted. In [START_REF] Zhang | Integral control on Lie groups[END_REF][START_REF] Maithripala | An intrinsic PID controller for mechanical systems on Lie groups[END_REF] concurrently, it has been proposed how to adapt (2) for Lie groups where U = g, i.e., under full actuation. Indeed, since u 0 is usually reflecting how much we push against the configuration error, it is proposed to replace (2) by

d dt u I (t) = -u 0 (t) . (3)
Thus, "the longer we have been pushing in the negative direction, the stronger we consider pushing in that direction". The equation always makes sense since both u 0 and u I belong to g, e.g., for rigid body motion they represent the rotational velocity and translation velocity expressed in body frame. It is proven that, for appropriate tuning, a first-order or second-order integrator on a Lie group can be stabilized towards u 0 = 0 and u I + u B = 0 with this approach.

The present paper addresses the case where u B ∈ U , such that it is not possible to just cancel it with the control input. For instance in steering control, if the translation velocity v differs from its nominal value, then this cannot be directly canceled by changing the value of our control input ω (rotation rate). In this case, we must find an objective that is compatible with the presence of u B ; this brings us to coordinated motion.

Coordinated Motion

For the case with u B = 0, we follow the framework of [START_REF] Sarlette | Coordinated motion design on Lie groups[END_REF], which was developed as a general theory behind proposals like [START_REF] Justh | Equilibria and steering laws for planar formations[END_REF][START_REF] Sepulchre | Stabilization of planar collective motion: All-to-all communication[END_REF] for steering control in the plane.

A set of rigid bodies is said to undergo a coordinated motion if their configuration at any time t is equal to their configuration at time 0, up to a symmetry transformation of the reference frame. In other words, the whole set moves together like a single rigid body. Ensuring coordinated motion can be seen as imposing symmetry on the behavior of the set of vehicles.

In the Lie group notation, coordinated motion of a vehicle g(t) with respect to a reference vehicle g ref (t) corresponds to keeping a fixed relative configuration

d dt (g -1 g ref ) = 0 .
In terms of velocities expressed in body frame, this is equivalent to

ξ = Ad g -1 g ref ξ ref , (4)
where Ad h is the adjoint action of h on the Lie algebra; in terms of matrices, we have ξ -1 = -ξ and Ad h (ξ) = hξh -1 . Note that this adjoint action is not just a rotation since h in general is not unitary: it really expresses which combination of translation and rotation velocities ξ, at a configuration g, would correspond to a coordinated motion with a vehicle applying ξ ref at configuration g ref .

Once ( 4) is achieved at a given time, keeping ξ ref and ξ fixed will ensure that it stays like this for all times, since by definition Ad g -1 g ref also stays fixed. Such situations are known as relative equilibria and they form basic primitives for further control [START_REF] Hanssmann | Symmetry and Reduction for Coordinated Rigid Bodies[END_REF]. For instance, for planar vehicles, we can apply the above with matrix expressions (1). Consider at time t a reference configuration g ref with (p x , p y , θ) ref = (1, 0, π/2) and following a circular trajectory of radius 1 around the origin, by applying ξ ref with (ω, v) = (1, 2π). Consider a tracking vehicle at configuration g with (p x , p y , θ) = (2, 0, π/2). Intuitively, to maintain coordinated motion, i.e., reference and tracking vehicles moving together like a single rigid body, the tracking vehicle would have to follow a circle of twice the radius of the reference. Mathematically, using matrix expressions (1), the relative configuration

g -1 g ref corresponds to Q -1 θ (p x , p y )ref -(p x , p y ) , θ ref -θ = (0, -1, 0)
, where Q θ is the 2 × 2 rotation matrix by angle θ in the upper-left block of g. This g -1 g ref expresses the relative position and orientation of the reference with respect to the tracking vehicle, in the reference frame attached to the tracking vehicle. Coordinated motion requires that this remains constant over time. The corresponding velocity that would achieve this for the tracking vehicle is computed by ( 4) with the matrix expression Ad

h (ξ) = hξh -1 ; it yields that ξ = Ad g -1 g ref ξ ref corresponds to (ω, v) = (1, 4π).
This is in agreement with motion on a circle of radius 2 around the origin. By computing the derivative of relative position and orientation in body frame, one can check that applying this velocity indeed implies d dt (g -1 g ref ) = 0, i.e., the result is consistent. The condition (4) for coordinated motion constrains g indirectly, in a combination with ξ. When ξ can be freely chosen in g, any relative position g -1 g ref is compatible with a relative equilibrium. However, for nonholonomic systems, where ξ is restricted to U , relative equilibria can restrict the set of relative configurations g -1 g ref , namely to the situations where Ad g -1 g ref ξ ref belongs to U (or more loosely if ξ ref can be adapted inside U , where the intersection of U with Ad g -1 g ref U is nonempty). In the case of a steering controlled vehicle (1) with v fixed and equal for all vehicles, there are two types of relative equilibria: parallel straight motion (ω ref = 0), where the underactuation restricts all the headings to be equal; and rotation on a common circle (ω ref = 0), where thus the underactuation restricts positions and headings to be properly aligned on that circle. Papers like [START_REF] Justh | Equilibria and steering laws for planar formations[END_REF][START_REF] Sepulchre | Stabilization of planar collective motion: All-to-all communication[END_REF] for planar vehicles, and [START_REF] Sarlette | Coordinated motion design on Lie groups[END_REF] more generally, have proposed control laws u = u 0 (g -1 g ref , ξ ref ) to stabilize such coordinated motion for perfectly calibrated systems.

Stabilizing the symmetric motion (4) thus leaves some freedom on the actual g and ξ. The freedom on g is most visible, namely for a given solution (g, ξ) any alternative ( g = hg, ξ), where h satisfies Ad h ξ = ξ is also a solution. This symmetry allows for complementing the controller stabilizing the coordinated motion by a controller that would specify particular relative configurations h, according to some other application needs and criteria. In addition, it is possible to satisfy (4) with a different ξ i.e., a different velocity applied in body frame. This would allow for satisfying coordinated motion even in the presence of biases, where ξ = A + u + u B . The possibilities depend on the range of Ad g ξ viewed as a function of g. For instance, when g represents an orientation (see satellite attitude control, [START_REF] Bai | Rigid body attitude coordination without inertial frame information[END_REF][START_REF] Sarlette | Autonomous rigid body attitude synchronization[END_REF]), Ad g corresponds to an orthogonal matrix and it is thus necessary that the available u and bias include the possibility ξ = ξ ref . For steering control, we must again distinguish two cases: if

ω ref = 0, then Ad g ξ ref = v ref for all g; whereas, if ω ref = 0, then the set {Ad g ξ ref : g ∈ G} = {ξ ∈ g : ω = ω ref }, i.e.
, as long as we apply the same angular velocity, and independently of the translation velocity, we can always find a configuration where coordinated motion is possible. In other words, {Ad g ξ : g ∈ G, ξ ∈ U } corresponds to all of g except the measure-zero set where ω = 0 and the translation velocity would differ from the one implied by A + u B , or, more concretely, the symmetry of coordinated motion can be restored, in principle, for any circular reference motion and any bias. The question is how to design a simple control algorithm that would stabilize this coordinated motion.

A "Moderating" Integral Controller (MIC)

In the absence of bias, designs u = u 0 (g -1 g ref , ξ ref ) have thus been proposed in e.g., [START_REF] Justh | Equilibria and steering laws for planar formations[END_REF][START_REF] Sepulchre | Stabilization of planar collective motion: All-to-all communication[END_REF] to stabilize coordinate motion. To explain our bias-rejection idea, we split the input in two terms u = u s + u m . The part u s corresponds to the steady-state motion once coordination is reached, i.e., there exists some h such that Ad

h(A + u s + u B ) = ξ ref , (5)
and thus once this situation is reached it suffices to apply u = u s to have a coordinated motion.

For the steering controlled vehicle, u s corresponds to ω s = ω ref .

The part u m in contrast steers the system towards a relative configuration h satisfying [START_REF] Sepulchre | Stabilization of planar collective motion: All-to-all communication[END_REF]. We denote by u P (g -1 g ref , ξ ref ) = u 0u s the corresponding part of the controllers proposed in [START_REF] Justh | Equilibria and steering laws for planar formations[END_REF][START_REF] Sepulchre | Stabilization of planar collective motion: All-to-all communication[END_REF]. In the absence of bias, u m = u P ensures that the vehicles asymptotically reach a coordinated motion, and so we also have u m = 0 asymptotically. In the presence of bias, our idea for restoring perfect symmetry is to add into the controller a term that explicitly targets u m = 0 asymptotically. More precisely, in the presence of a bias, u P (g -1 g ref , ξ ref ) = 0 will hold at configurations which do not correspond to a true coordinated motion; the configuration will thus change over time and, likely, u P will not remain zero. However, as u P does take a particular value u P ( h) at each point h which truly corresponds to a coordinated motion in the presence of bias, we can consider this value as a cancelable input bias and write an integral controller to cancel it:

u m = u P (g -1 g ref ) -k I u I (6) = u P ( h) + ( u P (g -1 g ref ) -u P ( h) ) -k I u I , d dt u I = u m .
Here, u m is thus known and can be considered as an observed output y which we want to stabilize to 0; in contrast, u P ( h) is not known because we do not know at which value of h the true coordinated motion holds, this is thus like an input bias; the remaining terms would be the effective controller, which involves u P (g -1 g ref )u P ( h) a proportional action taking the value zero at the true coordinated motion g -1 g ref = h as standard, and u I a standard integral controller like (2) with e(t) = y(t) = u m (t).

The modified integral controller u I in (6) thus somehow moderates the effect of u P , pushing to cancel its value at h.

Remark 1.

We have here assumed that the value of u s is known for which (5) is feasible, only the corresponding h is unknown. In the steering vehicle example, this comes down to assuming a possible bias on translation velocity, but assuming that rotation velocity is exactly known. We did this to focus on integral control for countering bias in unactuated directions. An unknown bias ω B in the actuated direction, yielding rotation velocity u = u 0 + ω B , could be solved with standard integral control and without the specificities of symmetries and coordination. We will come back to the slight adaptation needed for this case in the simulations section.

Steering Control Application

Explicit Nominal Dynamics with Coordination Control

We begin by writing (1) more concretely for the planar vehicle. Following [START_REF] Justh | Equilibria and steering laws for planar formations[END_REF][START_REF] Sepulchre | Stabilization of planar collective motion: All-to-all communication[END_REF], we thus consider a rigid body moving in the plane, with position p ∈ R 2 and heading θ ∈ S 1 the unit circle. We denote Q = Q θ the 2 × 2 rotation matrix by angle θ that forms the upper left block of g in [START_REF] Bai | Rigid body attitude coordination without inertial frame information[END_REF]. By denoting v = [v; 0] ∈ R 2 the fixed translation velocity in body frame, the Lie group dynamics (1) rewrites:

d dt Q = Q • Q π/2 • ω ; d dt p = Q v , (7)
where Q π/2 is the π/2 rotation. The control input thus reduces to u = ω, and we will assume that v can be subject to an arbitrary bias.

For the reference vehicle, we consider v ref = e 1 = [1 ; 0] without loss of generality. Under constant u = ω ref > 0, it rotates on a fixed circle of radius 1/ω ref and of fixed center

c ref = p ref (t) + Q π/2 Q ref (t)e 1 ω ref ∈ R 2
, which we assume c ref = 0 without loss of generality. By rewriting the objective (4) of coordinated motion Ad g ξ = Ad g ref ξ ref concretely, we get the physical requirements:

• equal rotation rate ω = ω ref ; • equal ω ref -conditioned center of rotation c = p(t) + Q π/2 Q(t)v ω ref = c ref = 0.
The rotation center c will play an explicit role in the analysis, its dynamics

d dt c = - Qv ω ref (ω -ω ref ) , (8)
just expresses that rotating at angular velocity ω around a fixed point is equivalent to rotating at angular velocity ω ref around a point that is itself turning.

The measurements performed towards feedback stabilization are the relative heading and relative position in body frame,

Q -1 Q ref and Q -1 (p ref -p), while ω ref is communicated to the vehicle. The nominal situation, without bias, corresponds to v = v ref = e 1 .
Feedback laws that stabilize (c, ω) towards (c ref = 0, ω ref ) in that case have been proposed in [START_REF] Justh | Equilibria and steering laws for planar formations[END_REF][START_REF] Sepulchre | Stabilization of planar collective motion: All-to-all communication[END_REF]. We here take for instance the simple "proportional controller" design of [START_REF] Sepulchre | Stabilization of planar collective motion: All-to-all communication[END_REF][START_REF] Sarlette | Coordinated motion design on Lie groups[END_REF]. For our purposes, we add saturation towards a better global behavior and introduce a notation with scaling parameter > 0 towards the convergence proof. With these elements, the proportional controller assuming v = e 1 , writes

u P = k P ω ref e T 1 σ[Q T (c -c ref )] (9)
= k P ω ref e T 1 σ Q T (p -p ref ) + Q π 2 (I 2 -Q T Q ref )e 1 ω ref .
Here, k P > 0 is the proportional control gain, I 2 denotes the 2 × 2 identity matrix; and σ : R 2 → R 2 is a saturation function:

σ[c] = c for c ≤ δ, δ c c for c > δ
for some appropriate δ ∈ R >0 . The saturation bounds the amplitude of feedback control actions w.r.t. the reference rotation rate ω ref .

Bias-Rejecting Controller and Proof Strategy

We now focus on countering biases on the un-actuated directions, i.e., on the translation velocity. We write v = |v|Q φ e 1 with any φ ∈ (-π/2, π/2) and assume that some bounds v m , v, χ > 0 are known on the velocity error, such that v ≥ v m , e 1v ≤ v and cos φ ≥ χ. These bounds will determine the acceptable saturation in u P which still allows for stabilizing the correct motion. Indeed, we will select δ ≥ 10 v/(χω ref ) in ( 9), although this is not a tight condition.

To reject the bias, we thus apply [START_REF] Aström | Feedback Systems: an Introduction for Scientists and Engineers[END_REF]; let us rewrite it for convenience:

u = ω ref + u P -k I u I , (10)
d dt u I = u P -k I u I ,
with u P given by the second line of [START_REF] Hanssmann | Symmetry and Reduction for Coordinated Rigid Bodies[END_REF]. Note that now this second line does not correspond to the true circle center c anymore because the latter would have to be computed with (unknown) v instead of e 1 , i.e.,

c = p + Q π 2 Qv ω ref . ( 11
)
This true circle center is unknown to the vehicle. For further reference, we denote by ĉ the (possibly wrong) circle center used in u P , i.e., computed by the vehicle assuming nominal dynamics with translation velocity equal to e 1 .

On that basis, we can make a concrete interpretation of the controller along the general lines of Section 2.3. Coordinated motion requires by definition ω = ω ref and c = c ref , independently of how the rest of the vehicle model is written. Thus, in the presence of bias, perfect coordination would still be obtained with a proportional controller that replaces ĉ by the true circle center c, that is if we

could use: ũP = k P ω ref v T Q T σ[c -c ref ] = k P ω ref v T σ Q T (p -p ref ) + Q π 2 v-Q T Q ref e 1 ω ref .
Since v is not known, the proportional controller features a bias u B = u P -ũP . When the saturation is inactive the expression simplifies and we obtain, with c ref = 0:

u B = k P ω ref (e T 1 Q T ĉ -v T Q T c) = -k P e T 1 Q π 2 v + k P ω ref (e 1 -v) T Q T c .
For later convenience, we rewrite this as

u B = ω B1 + ω B2 where ω B1 = -k P e T 1 Q π 2 v is a constant bias and ω B2 = k P ω ref (e 1 -v) T Q T c
is a state-dependent bias. This effective input bias can be viewed as the information missing for restoring symmetry. The integral controller can be seen as progressively deducing, based on observed deviations from symmetry over time, what is the value u B that must be countered. With this controller [START_REF] Sarlette | Coordinated motion design on Lie groups[END_REF], we get the closed-loop dynamics for the system:

d dt c = -k P e T 1 σ Q T θ c + Q π 2 (e 1 -v) ω ref - k I u I ω ref Q θ v, d dt u I = k P ω ref e T 1 σ Q T θ c + Q π 2 (e 1 -v) ω ref -k I u I , (12)
d dt θ = ω ref + k P ω ref e T 1 σ Q T θ c + Q π 2 (e 1 -v) ω ref -k I u I .
In the remainder of this section, we prove that this system indeed globally converges towards perfect coordinated motion.

Local stability of (12) under appropriate tuning can easily be proven by linearization around c = 0, k I u I = ω B1 . To rigorously prove global stability under appropriate tuning, we use averaging theory with estimation of the basin of attraction. The averaging method requires that the variable over which we average, here θ, is on a fast timescale 1/ω ref = O(1) with respect to the slow variables to be studied, here c, u I which evolve on a timescale 1/ , hence we will take 1. The proof proceeds in two steps:

•

First (Section 3.3), we study more accurately the local stability, specifying a region of exponential attraction (c, u I ) ∈ B r1 , a ball of radius r 1 > 0 around (c = 0, k I u I = ω B1 ) where saturation is inactive, and we fix a value 1 such that our conclusions are valid for all ≤ 1 . • Then, (Section 3.4) we analyze the system behavior for (c, u I ) ∈ W 2 = R 3 \ B r2 , with r 2 < r 1 , and we prove that for all ≤ 2 the system starting in W 2 will end up in B r1 .

This allows for concluding that the system converges globally for ≤ min( 1 , 2 ). Our conclusions are obtained from exhibiting a so-called Lyapunov function V whose scalar value is shown to decrease along solutions of the closed-loop system. We take:

V = 1 2 ( c 2 + µ| ũI | 2 ),
where µ > 0 has dimensions of area, and where

ũI = u I -ω B1 /k I = u I + k P k I e T 1 Q π 2 v
follows the same dynamics as u I . The balls are also defined with the µ-dependent metric, i.e., B r = {(c, ũI ) : . This implies essentially the same bound as for u P ,

c 2 + µ| ũI | 2 < r 2 }.
| k I u I (t)| ≤ (k P ω ref δ)ρ ( 13
)
for all t ≥ T, with T the time needed to leave the space |u

I | > ρ k P ω ref δ k I
, for any ρ > 1.

The time-derivatives of (c, u I ) are then obviously bounded by times some constants, while d dt θ equals ω ref up to adding the same constants times .

In the following Propositions, we will reduce our scope to what happens after we have reached

|u I | ≤ k P ω ref δ k I
ρ, with basically ρ 1. Lemma 1 allows for defining a (trajectory-dependent) bijection φ : t → θ, where θ ∈ R is interpreted keeping track of the number of turns. We abstractly write the first two lines of (12) as

d dt [c ; u I ] = h (φ) (c, u I , θ) = h(c, u I , t) (14)
and we will denote components by e.g., h = [h c ; h uI ]. Our strategy is thus to study the dynamics h of x = (c, u I ), by considering the fast dynamics of θ as a perturbation.

Local Exponential Stability

Theorems 2 and 3 in [START_REF] Aeyels | On exponential stability of nonlinear time-varying differential equations[END_REF] provide a way to deduce exponential stability of the original dynamics from the averaged dynamics. We first examine the averaged dynamics, analogously to part of the proof for Theorem 3 in [START_REF] Aeyels | On exponential stability of nonlinear time-varying differential equations[END_REF].

Proposition 1. Consider dynamics [START_REF] Khosravian | Observers for invariant systems on Lie groups with biased input measurements and homogeneous outputs[END_REF] corresponding to [START_REF] Bonnabel | Symmetry-preserving observers[END_REF] with fixed k P , k I , δ ≥ 10 v/χω ref , and let r 0 ≤ δv/ω ref . Then, for any µ > 0, there exist α > 0 and 1 > 0 such that for all ≤ 1 , there exists an increasing sequence of t

k (k ∈ Z) with t k → ∞ as k → ∞ and t k+1 -t k ≤ 4π/ω ref for all k ∈ Z, satisfying t k+1 t k ∂V ∂c T h c (c, ũI , s) + ∂V ∂ ũI h ũI (c, ũI , s) ds ≤ -αV (15)
for all k and for all (c, ũI ) ∈ W 0 := {c, ũI : c ≤ r 0 and |k

I u I | ≤ k P ω ref δ}.
Proof. The bound r 0 is chosen such that saturation is inactive. We define the sequence such that the interval [t k , t k+1 ) corresponds to a 2π rotation of θ. Using the bound in the proof of Lemma 1 and denoting κ = ρk P δ, we have (1

-2κ)ω ref ≤ d dt θ ≤ (1 + 2κ)ω ref , so 2π (1+2κ)ω ref ≤ (t k+1 -t k ) ≤ 2π (1-2κ)ω ref .
The sequence of t k then satisfies the requirements as soon as κ < 1/4 i.e., 1 < 1/(4k P δ). Now, the integral in [START_REF] Aguiar | Trajectory-tracking and path-following of underactuated autonomous vehicles with parametric modeling uncertainty[END_REF] corresponds to averaging over one period of θ, while keeping c and u I fixed. We use the bijection φ, with thus dφ/dt = ω > 0:

t k+1 t k ∂V ∂c T h c (c, ũI , s) + ∂V ∂ ũI T h ũI (c, ũI , s) ds, = 2π 0 (c T h (φ) c (c, ũI , θ) + µ ũI h (φ) ũI (c, ũI , θ)) 1 ω(c, ũI ,θ) dθ.
We decompose this into 1

ω = 1 ω ref + 1 ω ref ( ω ref -ω ω
), where the first term will give the desired result and the second term will be bounded. For the first term, we get:

2π 0 c T h (φ) c (c, ũI , θ)dθ = 2π 0 -k P (e T 1 Q T θ c) (c T Q θ v) dθ = -k P v π cos(φ) c 2 , ( 16
) 2π 0 ũI h (φ) ũI (c, ũI , θ))dθ = -2πk I | ũI | 2 . ( 17
)
There remains to account for (1

+ ω ref -ω ω ) = 1.
For c, we develop, using ( 12):

1 ω ref c T 2π 0 h (φ) c (c, ũI , θ) ( ω ref -ω ω ) dθ = 2π 0 c T Q θ v(k P e T 1 Q T θ c) 2 ω -2 k P e T 1 Q T θ cc T Q θ v k I ũI ω ref ω + k I ũI ω ref 2 c T Q θ v ω dθ ≤ c 3 k 2 P v 2π ω ref (1 -2κ) + 2 k 2 P c 2 v ( v /ω ref + δ)2π ω ref (1 -2κ) + β 2 ω ref 2π 0 c v cos(θ + φ -ψ) (1 -β) + k P c cos(θ -ψ) dθ .
Here, we defined β = k I ũI /ω ref and c = c [ cos ψ ; sin ψ ], and we used the bound ( 13) with ρ 1. The first two terms of the result are bounded by constants times c 2 , and hence they will not perturb the term from ( 16) once we take 1. For the last term, we can use the Taylor approximation of 1/(a + bx) for small x, with x = c , b = k P cos(θψ) and a = 1β. We can easily bound the higher order Taylor terms, so only the first order must be examined, namely:

β 2 ω ref 2π 0 c v cos(θ + φ -ψ) (1 -β) + k P c cos(θ -ψ) dθ β 2 ω ref (1-β) 2π 0 cos(θ + φ -ψ) v c dθ - 2 β 2 k P ω ref (1-β) 2 2π 0 cos(θ+φ-ψ) cos(θ-ψ) v c 2 dθ .
The first integral is zero and hence there only remains a term of order 2 c 2 , which again does not significantly perturb the term from [START_REF] Sanders | Averaging Methods in Nonlinear Dynamical Systems[END_REF]. For ũI , an analogous argument leads to terms of order ũ2

I and | ũI | c .
Taking 1 1 and selecting some minimum α 1 > 0 among coefficients, thus yields

t k+1 t k ∂V ∂x T h(x, s) ds ≤ -α 1 c 2 + ( √ µ ũI ) 2 2 -1 √ µ c | ũI | ,
which implies [START_REF] Aguiar | Trajectory-tracking and path-following of underactuated autonomous vehicles with parametric modeling uncertainty[END_REF] with α = α 1 (1 -1 ).

We have just established that V decreases exponentially under the average dynamics. The following result establishes local exponential stability of the original system. Proposition 2. Consider the same setting as in Proposition 1. Then, there exists 1 > 0 such that for all ≤ 1 , the equilibrium (c, ũI ) = 0 is locally exponentially stable. The region of attraction includes B r1 with

r 1 = exp -6Kπ 1 (1-2 1 k P δ)ω ref r 0 , where K = max( √ µω ref , v ) • max(k P , k I √ µω ref ).
Proof. We check that our system satisfies the conditions of Theorem 2 in [START_REF] Aeyels | On exponential stability of nonlinear time-varying differential equations[END_REF], such that exponential stability of the averaged system implies the same for the original system. Note that our balls correspond to the Euclidean norm of (c, √ µ ũI ). The condition about the Lyapunov function shape is then trivially satisfied by our quadratic V. The second condition, i.e., exponential stability of V under averaged dynamics, is checked in Proposition 1. Remark 5 following Theorem 2 in [START_REF] Aeyels | On exponential stability of nonlinear time-varying differential equations[END_REF] gives an associated estimate for the region of attraction, which includes ensuring that the trajectories stay in W 0 from Proposition 1. This estimate involves an exponential with T = 2π (1-2κ)ω ref from our Proposition 1 and a maximum Lipschitz constant on h(x). We estimate the latter, with our metric, through K to get the result.

Global Stability

For large c , the saturation complicates the analysis and precludes global exponential convergence of the Lyapunov function. We next consider this situation explicitly and show how V decreases for initial conditions (c, ũI ) outside a ball B r2 . Proposition 3. Consider the same setting as in Proposition 1. Let (c, ũI ) ∈ W 2 = {c, ũI :

c 2 + µ ũ2 I > r 2 and |k I u I | ≤ k P ω ref δ} with r 2 = 0.8δ. There exist µ > 0, α > 0 and 2 > 0 such that, for all < 2 , if the system stays in W 2 for t ∈ [0, T = 1/ ], then V(T) < V(0) -α. Proof. We divide V(T) -V(0) into two contributions ∆V 1 = V(Φ T (c, ũI )) -V(c, ũI ) and ∆V 2 = V(Φ T (c, ũI )) -V(Φ T (c, ũI )).
Here, Φ T (x 0 ) denotes the result at time T of integrating the dynamics [START_REF] Bonnabel | Symmetry-preserving observers[END_REF] with initial conditions (x 0 , θ 0 = 0), and Φ T (x 0 ) is the result of integrating the averaged dynamics. Since we are now working away from x = (c, ũI ) = 0, we can ensure that terms of order 2 and higher can be dominated by e.g., x ; this facilitates the analysis compared to Proposition 1. In particular, we will take ω = ω ref + O( ).

∆V 2 thus gives the difference between the result of the actual and average dynamics. According to the fundamental theorem of averaging (Theorem.2.8.1 in [START_REF] Sanders | Averaging Methods in Nonlinear Dynamical Systems[END_REF]), keeping error estimates, there exists 2 > 0 such that for all ≤ 2 we have

Φ T -Φ T ≤ C 1 e KL for fixed L = T, where K ≤ max( √ µω ref , v ) • max(k P , k I √ µω ref
) is a maximum Lipschitz constant on h(x) and C 1 is a bound on the integrated difference in vector flow between average and original system. From Lemma.2.8.2 in [START_REF] Sanders | Averaging Methods in Nonlinear Dynamical Systems[END_REF], we get C 1 ≤ (2 + KL) max x h(x) /ω ref where, thanks to saturation and (13), we have max

x h(x) ≤ 2k P δ( v + √ µω ref ). We can further bound Φ T (x) -x ≤ L max x h(x) ≤ 2k P δ( v + √ µω ref )
L and the same for the average dynamics. Since V(Φ T (x)) = 1 2 Φ T (x) 2 , we thus get, for fixed L = T and ≤ 2 ,

∆V 2 (x) ≤ Φ T (x) -Φ T (x) • 1 2 Φ T (x) + Φ T (x) ≤ 2 (2+KL)k P δ( v + √ µω ref ) ω ref e KL (18)
•

x + 2k P δ( v + √ µω ref ) L .
Note that x ≤ √ µ| ũI | + c where ũI is bounded. ∆V 1 is the gain in Lyapunov function value under the average dynamics.

For ũI , we observe that by symmetry

2π 0 σ(Q T θ c + Q π 2 (e 1 -v) ω ref )dθ = 2πβ Q π 2 (e 1 -v) ω ref
with β ∈ (0, 1). This implies that the contribution of µ

2 ũ2 I to ∆V 1 is at most µ 2 (( ω B1 k I ) 2 -ũI (t) 2 ) + O( ) =: µβ c + O( ), for any integration time.
For c, by considering the term

Q π/2 e 1 -v ω ref
inside the saturation as a disturbance on c, we can establish that the average dynamics would follow:

d dt av c = -k P v δ 2ω ref c (Q φ c + γ) + O( 2 ) i.e., 1 2
d dt c 2 ≤ -k P δ v 2ω ref (cos φ c -γ ) + O( 2 ) , (19)
with γ ≤ 4 v ω ref . Knowing that | ũI | 2 ≤ k 2 P ω 2 ref k 2 I δ + v ω ref 2 and v ω ref < δ/10, a state in W 2 implies c 2 ≥ 0.8 2 -1.1 2 µk 2 P ω 2 ref k 2 I δ 2
and we can e.g., select µ <

(0.8 2 -0.5)k 2 I 1.1 2 k 2 P ω 2 ref to ensure c 2 ≥ δ 2 /2. Then, for δ ≥ 10 v ω ref χ , we have γ < 0.6 10 v √ 2ω ref ≤ 0.6 cos φ c .
Inserting this bound on γ into (19), we thus obtain

d dt c ≤ -0.4 k P δ v χ 2ω ref + O( 2 ) =: -α c + O( 2 ) .
Over a period T, with T = L, the corresponding contribution of 1 2 c 2 to ∆V 1 (x) is negative with a magnitude at least 1 2

( c 2 ) -1 2 ( c -α c L + O( )) 2 = c α c L -1 2 (α c L) 2 + O( ) c
. Taking all contributions together and fixing L = 1, we obtain

∆V ≤ -α c c + 1 2 α 2 c + µβ c + O( ) + O( ) c , with c > δ/ √ 2.
Selecting µ < δ 2 /4β c ensures that the part without is (sufficiently) negative, and then a sufficiently small obviously yields the result.

We now have all the elements to state our main result. Theorem 1. There exists 0 > 0 such that, for all 0 < ≤ 0 , the controlled system (12) with fixed k P , k I , δ ≥ 10 v/χω ref converges globally to (c, ũI ) = 0.

Proof. For given r 0 = δv/ω ref and parameter values, we can choose 3 > 0 such that ≤ 3 implies r 1 > 0.9r 0 ≥ 0.9δ(1χ/10) ≥ 0.81δ in Proposition 2. We can then take r 2 > 0.8δ as required by Proposition 3, while satisfying r 2 < r 1 . Now taking the µ, 1 , 2 from Proposition 1 and Proposition 3, select some ≤ 0 = min( 1 , 2 , 3 ).

•

By Lemma 1, after an initial transient, the system reaches the subset where the bound (13) holds and stays there for all future times.

•

Assume by contradiction that the system would stay in W 2 for all times. Then, by Proposition 3, the Lyapunov function would decrease like V(t) < V(0)t α for some constant α > 0 for all times, which is not possible since V must stay positive (in fact even larger than r 2 2 /2 if we stayed in W 2 ). Thus, the system cannot stay in W 2 forever.

•

This means that, at some time, the system must enter B r2 , and thus it also enters B r1 since r 1 > r 2 .

Once the system is in B r1 , we know by Proposition 2 that it converges to (c, ũI ) = 0 exactly.

Simulations

The benefit of our modified integral controller is illustrated with the following simulations. The reference vehicle (black on the Figures) moves on Figure 1 (left) shows the vehicle behavior (blue on the Figures) when applying u = u 0 the proportional controller derived for the nominal model, or in other words [START_REF] Bonnabel | Symmetry-preserving observers[END_REF] with k I = 0. After a short initial transient, the motion of the vehicle does converge to rotation on a circle centered at the origin, but not at the correct speed. Indeed, the tracking vehicle keeps accumulating a phase advance with respect to the reference. This is because, due to the bias, the controller thinks that it has not converged to the correct configuration yet, and keeps pushing. As a result, the symmetry of coordinated motion is broken: the relative configuration between the following vehicle and the reference is continuously changing, with macroscopic long-term effects. This is rather problematic for a controller whose very purpose is to stabilize coordinated motion. Our controller corrects this effect, as illustrated on Figure 1 (right) with tuning parameters k P = 1, k I = 0.1 and δ = 0.2. Indeed, after the initial transient during which the vehicle has to converge towards the correct circle, it moves with a fixed phase lag with respect to the reference, thus in coordinated motion. Figure 2 (left) shows a typical vehicle trajectory when starting farther away, and it confirms more visually that the vehicle converges to the correct circle (the fact that the vehicle is then rotating at the correct rate must be taken from other plots). Figure 2 (right) confirms the convergence of the true circle center c towards c ref = 0, which by definition (check [START_REF] Maithripala | An intrinsic PID controller for mechanical systems on Lie groups[END_REF] and the corresponding remark) should mean that it is rotating at the correct rate on the correct circle. The convergence is first linear due to the feedback saturation, then exponential once |c| < δ. The figure also shows that, while u P still thinks that it has to apply a correction (due to the bias between the true c and the ĉ computed with the assumption v = e 1 by the controller), the modified integral controller progressively understands that it has to cancel it such that the total control input reduces to u = ω ref .

We next include an unknown bias on the angular velocity too. The rotation velocity of the vehicle thus now corresponds to On the upper right plot, horizontal lines are provided as guides to the eye to witness that the integral input asymptotically cancels the remaining ûP .

ω = u + ω B = ω ref + u P -k I u I + ω B = ω ref + ( u P (g -1 g ref ) -u P ( h) ) -k I u I + u P ( h) + ω B .
The total effective bias thus now corresponds to u P ( h) + ω B instead of just u P ( h). In other words, if we can drive g -1 g ref towards h and -k I u I + u P ( h) + ω B towards 0, then we reach a perfect coordinated motion. It is tempting to view ω B and u P ( h) as just two similar contributions to the total bias, and thus by analogy we would just use the integral controller d dt u

I = u P (g -1 g ref ) -k I u I + ω B .
The issue is that the controller a priori does not have access to the value of ω B . However, the controller does observe the relative heading θ(t)ω ref t of the vehicle with respect to the reference. Remarkably, since d dt θ(t) = ω, we see that the integral term which we want to use in fact just corresponds to

u I (t) = θ(t) -ω ref t .
Thus, for the particular application of steering control, the general strategy in fact simplifies to feeding back a directly observed quantity. Figure 3 shows a simulation of this situation, with thus now biases on all velocities. We observe that the proposed modified integral controller indeed restores the symmetry of coordinated motion perfectly. The presence of ω B , with all other parameters and initial values kept as before, just translates into a different steady-state relative phase between reference and leader vehicle. A standard integral controller like (2) would require to integrate the true error y, which here is not measured. The hope is then to correct the situation by integrating instead the input corrections w. Indeed, the target includes the requirement w = 0; and, if the effect of q = 0 is observable on z (and this is an important requirement), then by monitoring the corrections that we apply we should learn something about q. This motivates the same modified integral controller as proposed for coordinated motion:

w = u P -k I u I , ( 20
) d dt u I = w .
If this controller reaches a steady state, then we have achieved half our goal namely w = 0. A further observability/invariance property of the plant is necessary to ensure that we stabilize the full target. Namely, if states with (w = 0, z = z(q) ) are not invariant, while (w, zz(q) ) = 0 is invariant, then we can hope to show convergence towards f (z, q) = f ( z(q) , q) = 0 and k I u I = u B . This last requirement shows that (20) is likely to work, in general, if (w, y) = 0 is a natural equilibrium and the purpose of the controller is to stabilize it, while avoiding that the feedback based on measurement z displaces the natural equilibrium from y = 0.

We can make this more explicit on a linear system. Consider in Laplace domain z(s) = y(s) + q(s) = H(s) (w(s) + b(s)) + q(s) where the goal is to stabilize y(s) = 0, while b(s) is some actuation noise and q(s) is noise on the output measurement [START_REF] Aström | Feedback Systems: an Introduction for Scientists and Engineers[END_REF]. With a controller w(s) = C(s)z(s), we obtain the closed-loop equation For C(s) = 0, the measurement noise is of course not transmitted to y. However, a controller might be necessary for stabilizing the system or rejecting disturbances b according to frequency-dependent specifications.

At each frequency, strongly attenuating the effect of b requires a large value of HC, which implies that q is transmitted into y with almost unit gain. Thus, to perfectly counter a bias in the input, you must be able to trust the value of the measurement, and vice versa. A standard proportional-integral controller makes |C(s)| = |k I /s + k P | infinite at zero frequency, implying perfect rejection of b(0). Conversely, our modified integral controller is well-adapted to applications where q poses major problems at low frequencies while b(0) is low. Indeed, the MIC with u P = K(s)z writes C(s) = K(s) s s + k I .

For frequencies |s| k I , we have C(s) K(s). For small s, however, the controller is attenuated, and in particular when H(0)K(0) is finite the MIC ensures

H(0)C(0)
1-H(0)C(0) = 0 i.e., perfect rejection of q(0). The stability analysis must (only) investigate the changes implied at those low frequencies. Practical situations where it is relevant to reject q(0) rather than b(0) could appear when drifts of the sensor-zero cannot be calibrated, e.g., a shift induced by a failure event or by fluctuations of operating conditions. Remark 2. The integrator with leakage as used in [START_REF] Gorinevsky | Design of low-bandwidth spatially distributed feedback[END_REF] would instead yield C(s) = K(s)(s + a) + k I s + a with a ≥ 0 the leakage parameter. For a = 0, one recovers an integral controller. For large a, we get C(s) K(s) at all frequencies. This does a priori not guarantee any specific transmission property for q(s); the purpose of a leaking integrator is indeed rather to ensure more robust stability.

Conclusions

In this paper, we have investigated how a simple modified integral control (MIC) allows for restoring perfect coordination symmetry in the motion of steering controlled vehicles subject to actuation bias. The non-holonomic dynamics of such systems make it impossible to directly counter the bias. However, the conditions for perfect symmetry to be achievable are clear in a Lie group framework, and an adaptation of integral control to this situation is proved to yield perfect global convergence.

This control perspective on symmetry, more particularly on restoring it, seems to rely on stabilizing a manifold of configurations where symmetry holds, rather than formulating the problem directly as a tracking task. We have also discussed how this MIC might address other tasks, related to biases in output measurements. The common framework is that the target situation would be a natural equilibrium of the system, a feedback control action is used in order to stabilize it, and we must avoid that errors in the model used by the controller displace the steady state from the target situation.

Of course, more powerful state-based methods could also be devised, explicitly estimating all the model errors and biases and specifying according actions-see, e.g., [START_REF] Aguiar | Trajectory-tracking and path-following of underactuated autonomous vehicles with parametric modeling uncertainty[END_REF]. However, much like for standard integral control, we believe that simple control "gadgets" like the MIC can have their own interest in terms of plug-and-play robustness (no need for an extensive system model in the controller), simplicity of implementation (e.g., in analog hardware), and therefore maybe providing insight into how some natural systems could be wired in order to preserve perfect symmetries. Also note that, like standard integral control, the strategy should remain applicable if the bias is state-dependent, which would be harder to formulate with explicit estimation.

  a circle of radius 0.1 with rotational speed ω ref = 10 and unit translational velocity v ref = [1; 0]. The tracking vehicle has a biased translational velocity v = [1; 0.3].

Figure 1 .

 1 Figure 1. Left: Evolution of position p over time with nominal-case controller (k I = 0): reference (black) and actual motion (blue) undergo a progressive dephasing, i.e., a small error breaks the symmetry of coordinated motion. Right: Evolution of position p over time with our modified integral controller: the perfect coordinated motion of reference (black) and actual motion (blue) is restored.

Figure 2 .

 2 Figure 2. With modified integral control, left: trajectories of position p and center c = p + Q π/2 Qv/ω ref . Right: convergence of the proportional input ûP , integral input k I u I and deviation of circle center |c|.On the upper right plot, horizontal lines are provided as guides to the eye to witness that the integral input asymptotically cancels the remaining ûP .

Figure 3 .

 3 Figure3. Evolution of position p over time with our modified integral controller, when adding as well an actuation bias ω B on the actuated part of the velocity. This corresponds to biases everywhere, and the correcting integral controller appears to boil down to u I (t) = θ(t)θ(0). The perfect coordinated motion of reference (black) and actual motion (blue) is restored in that case too.

  For fixed k P , k I , δ, ω ref , any initial conditions, and any C > 0, we can ensure that d dt θ > C max( d dt c(t) , | d dt u I (t)|) after some initial time by selecting a small enough > 0. Proof. Since |u P (t)| is upper bounded by k P ω ref δ, we have =k I u 2 I + u P u I ≤ -(k I u 2 Ik P ω ref δu I ) and |u I | can only decrease when |u I | > k P ω ref δ

	d dt u 2 I /2 k I

Before proceeding, let us confirm that averaging over θ makes sense. Lemma 1.

Funding: This research was funded by China Scholarship Council grant and complementary UGhent-BOF grant number 01N00213.

Towards Other Applications of the Modified Integral Controller

The modified integral controller [START_REF] Aström | Feedback Systems: an Introduction for Scientists and Engineers[END_REF] may have other applications. We briefly discuss this in a more abstract system-theoretic context.

Consider the following abstract setting, see Figure 4:

• a system with state x(t), variable of interest y(t), must be stabilized to some target (y, w) = 0 by the control command w(t).

•

unlike standard, the value of y(t) is not measured, but deduced from actual measurements z(t) through the static function y = f (z, q) where q are some constant parameters.

•

the exact value of q is not known; without loss of generality, q = 0 is nominal and f (0, 0) = 0.

The target variable y is thus not available to the controller, while w is known. This setting could appear in various applications, as a consequence of a residual calibration error or of a sensor fault. The stabilization of coordinated motion can be formulated in this framework with

•

x the rigid body configuration g, for the planar vehicle (p, θ);

is the target variable to be put to zero, according to (4). For the vehicle, it has three scalar components (Q

Referring to [START_REF] Aeyels | On exponential stability of nonlinear time-varying differential equations[END_REF], the others take the form

indeed involving known constants, the unknown q, and observed variables z.

The goal is thus to propose a simple control mechanism which would automatically cancel the error on y associated with q = 0, without requiring advanced logic like an observer estimating sensor parameters.

- We repeat a reasoning similar to the one of Section 2.3. Assuming the nominal situation q = 0, a well-tuned proportional feedback w = u P (z) would typically drive z to zero and thus y towards f (0, 0) = 0, at least locally. However, if q = 0, then w = u P (z) = 0 does not correspond to y = 0 anymore. To perfectly reach the target (y, w) = 0, the controller should instead apply w = u P (z)u P ( z(q) ) , where z(q) is a measurement value for which f ( z(q) , q) = 0. Because q is not known, the value of z(q) is not known either. However, in this formulation, the effect of q can be viewed just as an unknown input bias u B = u P ( z(q) ) to be canceled.

Author Contributions: conceptualization, Z.Z., A.S. and Z.L.; methodology A.S.; software, formal analysis, writing and editing, Z.Z. and A.S.; supervision A.S.; resources, project administration, funding acquisition A.S. and Z.L.

Conflicts of Interest:

The authors declare no conflict of interest.


B Jayawardhana
email: b.jayawardhana@rug.nl

M Z Almuzakki
email: m.z.almuzakki@rug.nl

A Tanwani
email: aneel.tanwani@laas.fr

Practical Stabilization of Passive Nonlinear Systems with Limited Control

Keywords: Nonlinear passive systems, finite control set, output feedback, binary control

This article addresses output feedback stabilization of continuous-time nonlinear systems by choosing control actions from a finite set. Working under the assumption that the system under consideration is passive and large-time norm observable, we propose a static feedback mapping, from the output space to the finite set of control actions, which is shown to be practically stabilizing if the convex hull of certain control actions (in the chosen finite set) contains the origin in its interior. Consequently, to construct this stabilizing feedback, it suffices to have, in addition to a zero symbol, another m + 1 elements in the control set which form an m-simplex in R m (the input, and output space).

INTRODUCTION

The design of advanced mechatronics and robotics systems often has to deal with extreme environmental conditions, such as, cryogenic temperature or ultra-high vacuum setting, and consequently several design constraints have to be taken into account. These constraints restrict the choice of the sensor systems, actuator systems, as well as, the information processing and control mechanism. For instance, in a networked robotics systems, the limited capacity of the communication network enforces the realtime information from the sensor systems and from the controller to be quantized before it can be transmitted via the network. The quantization process produces information loss that can lead to a performance degradation of the overall systems. Among many others, the seminal paper [START_REF] Elia | Stabilization of linear systems with limited information[END_REF] discusses the design of control systems under minimal information / quantization levels for linear single input systems case. The study was continued in [START_REF] Kao | Stabilization of linear systems with limited information multiple input case[END_REF] for multiple input case of linear systems. In the networked control systems setting, the papers [START_REF] Cortés | Finite-time convergent gradient flows with applications to network consensus[END_REF][START_REF] Jafarian | Formation control using binary information[END_REF][START_REF] De Persis | Coordination of passive systems under quantized measurements[END_REF] present various analysis and design methods that incorporate the quantization effect in the control design. Another example is the design of mechatronics systems with limited actuation, such as, a fixed set of constant actuator systems in Ocean Grazer [START_REF] Barradas-Berglind | Energy capture optimization for an adaptive wave energy converter[END_REF][START_REF] Wei | Investigating the adaptability of the multi-pump multi-piston power take-off system for a novel wave energy converter[END_REF], or a fixed configuration of constant thruster systems in the space rockets, which can only provide piecewise constant actuation with limited discrete values. In this paper, we shall address the constrained control problem, where the The work of A. Tanwani is partially supported by ANR project ConVan, grant number ANR-17-CE40-0019-01.

focus is on designing control actions with limited information from sensors, and limited/minimal actuation levels.

To describe our control problem, let us consider linear systems described by

Σ lin : ẋ = Ax + Bu y = Cx (1)
where the state x(t) ∈ R n , the input and output signal u(t), y(t) ∈ R m and (A, B, C) are system's matrices with appropriate dimension. As we consider limited actuation / information transmission, the control input u can only take values from a finite discrete set U := {0, u 1 , u 2 , . . . , u p } with u i ∈ R m for each i = 1, . . . , p. For such systems, assuming we have a stabilizing output feedback law y → Ky, two questions are particularly relevant for stabilization when the actuation set U is finite: a) how to map Ky to an element in U and b) how to determine the minimal number of elements in U. By addressing these questions, with generic output maps and nonlinear dynamics, our aim is to design φ : R m → U, with U discrete (and minimal), such that u = φ(Ky) ∈ U practically stabilizes Σ lin .

The question of finding the minimal set U for feedback stabilization has received considerable attention. The first step in this regard is, what should be the minimal cardinality of the set U. The primary results in this direction state that if the number of bits per sample (rate of communication) is greater than the intrinsic entropy of the system, then the system is stabilizable in some appropriate sense. Such relations are collected in the discrete-time linear setting by [START_REF] Nair | Feedback control under data rate constraints: An overview[END_REF]. For the continuoustime systems, the articles by [START_REF] Colonius | Invariance entropy for control systems[END_REF] and [START_REF] Colonius | Minimal bit rates and entropy for exponential stabilization[END_REF] develop similar relations for linear systems, but their bounds on minimal bit-rates for stabilization of nonlinear systems are rather conservative.

The question of designing the mapping φ : R m → U has also been addressed in different forms. Note that, with U being discrete, the range of φ determines a partition of the output space containing as many regions as the number of elements in the set U. When U is a regular grid in a compact set in R m , for example {-N, -N + 1, . . . , N -1, N } m with 2N + 1 being the number of quantization level per input dimension, then φ can be the standard (uniform) quantization operator. In this case, the existing works on quantized control systems are directly applicable.

We refer the works of [START_REF] Delchamps | Stabilizing a linear system with quantized state feedback[END_REF][START_REF] Tatikonda | Control under communication constraints[END_REF][START_REF] Ceragioli | Discontinuous stabilization of nonlinear systems: Quantized and switching controls[END_REF][START_REF] Fu | State estimation for linear discrete-time systems using quantized measurements[END_REF][START_REF] Jafarian | Formation control using binary information[END_REF][START_REF] De Persis | Coordination of passive systems under quantized measurements[END_REF]. However, in these works, the state is shown to stay in a ball around the origin whose size depends on the number of quantization levels. One has to resort to timevarying feedbacks to get desired accuracy with finitely many quantization levels [START_REF] Liberzon | On stabilization of linear systems with limited information[END_REF][START_REF] Tanwani | Observerbased feedback stabilization of linear systems with event-triggered sampling and dynamic quantization[END_REF]. However, we will show in Section 3 of this paper that a static mapping that assigns Ky to the nearest element in U (practically) stabilizes the system under certain structural assumptions.

With certain passivity structure in the dynamics, Σ lin can be practically stabilized by using binary control for each input dimension which translates to 2 m + 1 elements in U, e.g., U = {0} ∪ {-1, 1} m , see [START_REF] Cortés | Finite-time convergent gradient flows with applications to network consensus[END_REF][START_REF] Jafarian | Formation control using binary information[END_REF]. As a relaxation of these results, and dealing with multi-input multi-output nonlinear systems, we show that practical stabilization is achievable by simply using m + 2 elements in U for a rather generic class of passive systems.

Passive systems are well-studied in literature on dynamical control systems, as they model physical phenomena exhibited by almost all thermo-chemo-electromechanical systems (van der [START_REF] Van Der Schaft | On the mathematical structure of balanced chemical reaction networks governed by mass action kinetics[END_REF][START_REF] Ortega | Passivity-based Control of Euler-Lagrange Systems[END_REF].

The passivity property can be related to the dynamics of the energy variable of a system. It describes the energy dissipation process and energy exchange mechanism with the environment through the (input/output) ports. In particular, for passive systems, the rate of change of the system's "stored energy" never exceeds the power supplied by the environment through its external ports. We refer interested readers to the various expositions on passive systems in [START_REF] Sepulchre | Constructive Nonlinear Control[END_REF][START_REF] Ortega | Passivity-based Control of Euler-Lagrange Systems[END_REF][START_REF] Khalil | Nonlinear Systems[END_REF][START_REF] Van Der Schaft | L2-Gain and Passivity Techniques in Nonlinear Control[END_REF].

When quantization effect is of a particular concern, the interconnection of passive systems and quantizers has been studied for the past decade in various different contexts. The tutorial paper [START_REF] Jayawardhana | The circle criterion and input-to-state stability[END_REF] presents the practical stability analysis of passive systems in a feedback loop with a quantizer using an adapted circle criterion for nonsmooth systems. For distributed control systems, the paper (De Persis and Jayawardhana, 2012) analyzes networked passive systems through a quantized communication channel. The contribution of this paper lies in studying control strategies for passive systems with minimal control actions.

The rest of the paper is organized as follows. In Section 2, we provide some preliminaries on set-valued dynamics re-sulting from discontinuous controls, and formulate the control problem. Our main results are presented in Section 3, where we study practical stabilization of passive systems under the nearest neighbor control approach. Illustration via an academic example and concluding remarks are provided in Sections 4 and 5, respectively.

PRELIMINARIES AND PROBLEM FORMULATION

Preliminaries

Notation: For an element of a normed vector space, we denote its norm by

• . The set B ⊂ R n is defined as, B := {ξ ∈ R n | ξ ≤ }.
The inner product of two vectors ν, γ ∈ R m is denoted by ν, γ . For a discrete set U, its cardinality is denoted by card(U). The convex hull of vertices from a discrete set B is denoted by conv(B). The interior of a set S ⊂ R n is denoted by int (S). For a signal z : R + → R n , the essential supremum norm of z over an interval I ⊂ R + is denoted by z I .

A set-valued mapping Φ :

R n ⇒ R n is called upper semicontinuous at x if for every open set X containing Φ(x) ⊂ R n , there exists an open set Ξ containing x such that for all ξ ∈ Ξ, Φ(ξ) ⊂ X. Correspondingly, Φ is upper semicontinuous if it is upper semicontinuous at every point in R n . Using the set-valued map Φ, consider now the following differential inclusion ẋ ∈ Φ(x) x(0) = x 0 .
(2) A Krasowskii solution x(•) on an interval I ⊂ R + is an absolutely continuous function x : R + → I such that (2) holds almost everywhere on I. It is maximal if it has no right extension and it is a global solution if I = R + . For any upper semicontinuous set-valued map Φ such that Φ(ξ) is compact and convex for all ξ ∈ R n , the following properties have been established (see, e.g., Lemma 1 in [START_REF] Jayawardhana | The circle criterion and input-to-state stability[END_REF]): (i). the differential inclusion (2) has a solution; (ii). every solution can be extended to a maximal one; and (iii). if the maximal solution is bounded then it is global. For any discontinuous map F : R n → R n , we can define an upper semicontinuous set-valued map Φ by convexifying F as follows

K(F (x)) := δ>0 co(F (x + B δ ))
where co(S) is the convex closure of S.

Stabilization with limited control problem

For the rest of this paper, we consider the following nonlinear systems

Σ : ẋ = f (x) + g(x)u y = h(x) (3)
where the state x(t) ∈ R n , the output y(t) ∈ R m and the input u(t) ∈ U := {0, u 1 , . . . , u p } with u i ∈ R m for all i = 1, . . . , p. The function f, g and h are assumed to be C 1 differentiable, f (0) = 0, g(x) is full-rank for all x and h(0) = 0. We assume further that Σ is passive, i.e., for all pair of input and output signals u, y we have T 0 y(t), u(t) dt > -∞ for all T > 0 [START_REF] Willems | Dissipative dynamical systems. Part I: General theory[END_REF][START_REF] Van Der Schaft | L2-Gain and Passivity Techniques in Nonlinear Control[END_REF][START_REF] Ortega | Passivity-based Control of Euler-Lagrange Systems[END_REF]. By the well-known Hill-Moylan conditions, the passivity of Σ implies that there exists a positive definite storage function H : R n → R + such that ∇H(x), f (x) ≤ 0 and ∇H(x), g(x) = h (x). Without loss of generality, we assume that the storage function H is proper, i.e. all level sets of H are compact.

Using the passivity assumption on Σ, it is immediate to see that u ≡ 0 implies that all level sets of H are positively invariant. More precisely, for any c > 0, if H(x(0)) ≤ c then H(x(t)) ≤ c for all t ≥ 0. In other words, if we initialize the state of Σ such that x(0) ∈ Ω c := {ξ|H(ξ) ≤ c} with u ≡ 0 then x(t) ∈ Ω c for all t ≥ 0. We will use this property later to establish the practical stability of our closed-loop systems in conjunction with the following observability notion from [START_REF] Hespanha | Nonlinear norm-observability notions and stability of switched systems[END_REF]. System (3) has the large-time norm observability property if there exist τ > 0, γ, χ ∈ K ∞ such that the solution x of (3) satisfies

x(t) ≤ γ( y [t1,t2] ) + χ( u [t1,t2] ) for all x(0), u, t 1 ≥ 0, t 2 ≥ t 1 + τ , and each t ∈ [t 1 , t 2 ].
In particular, we will use the large-time norm observability property for the autonomous system (with u = 0):

ẋ = f (x) y = h(x). (4)
In this case, large-time norm observability of (4) implies

∃ τ > 0, γ ∈ K ∞ such that, for each t 1 ≥ 0, x(0) ∈ R n , x(t) ≤ γ( y [t1,t2] ), ∀t 2 ≥ t 1 + τ, t ∈ [t 1 , t 2 ]. (5)
We note that in the standard passivity-based control literature, the notion of zero-state observability or zerostate detectability is typically assumed for establishing the convergence of the state to zero in the Ω-limit set. However, these notions cannot be used to conclude the boundedness of the state trajectories given the bound on the output trajectories. Therefore, instead of using these notions, we will use the above large-time norm observability for deducing the practical stability based on the information on y in the Ω-limit set.

Practical output-feedback stabilization with limited control (POS-LC): For a given system Σ as in (3) and for a given ball B with > 0, determine the (minimal) elements u i , i = 1, . . . , p for U and determine φ : R m → U such that the closed-loop system of (3) with u = φ(y) satisfies x(t) → B as t → ∞ for all initial condition x(0).

In our problem formulation, both the determination of elements in U, as well as, the design of the practical stabilization mapping φ constitute our control problem.

To solve this problem, we will work under the following basic assumption:

(A0) The system Σ in (3) is passive with a proper storage function H and, the corresponding autonomous system (4) is large-time norm-observable for some τ > 0 and γ ∈ K ∞ .

NEAREST NEIGHBOR CONTROL

As a solution to the aforementioned problem, we provide conditions on the set U followed by the description of the static map φ which practically stabilizes the system. For efficiency in practical implementation, in our design, we work with minimal number of elements in the set U which yield the desired performance using the static feedback only. Toward this end, the only assumption we associate with the set U is the following:

(A1) For a given set U := {0, u 1 , u 2 , . . . , u p }, there exists a minimal index set I ⊂ {1, . . . , p} such that the set V := {u i } i∈I ⊂ U defines the vertices of a convex polytope satisfying, 0 ∈ int (conv (V)).

An immediate consequence of (A1) is the following lemma, which is used in the derivation of our main result. Lemma 1. Consider a discrete set V := {v 1 , . . . , v q } ⊂ R m such that 0 ∈ int(conv (V)). Then, there exists C V > 0 such that the following implication holds for each

y ∈ R m y > C V ⇒ ∃ v i ∈ V s.t. v i + y < y . (6)
Proof. First we observe that any point ṽ ∈ R m that is closer to the origin than to any point v i ∈ V is contained in the solution set of the linear inequality

[v 1 . . . v q ] ṽ ≤ 1 2 v 1 2 . . . v q 2 (7)
where v 1 , . . . , v q ∈ V. Let us denote the solution set of (7) by V. In other words, the set V is defined by the intersection of q half-spaces described by each row of the inequality (7), so that V is a closed convex polyhedron. It remains to show that V is bounded. Indeed, boundedness implies that we can choose

C V = max ṽ∈ V ( ṽ ), such that B C V is the smallest ball containing the set V. By construction of V, for any point y ∈ R m \ B C V , there is an element v i ∈ V such that v i + y < y .
To show that V is bounded, we observe that, under (A1), there exists δ > 0 such that B δ ⊂ conv(V). Thus, for every ṽ ∈ V, δ ṽ ṽ ∈ conv(V). Hence, there exist λ i ≥ 0 such that q i=1 λ i = 1 and δ ṽ ṽ = q i=1 λ i v i . Consequently, from (7), it follows that δ ṽ ṽ ṽ =

q i=1 λ i v i ṽ ≤ 1 2 q i=1 λ i v i 2 and hence ṽ ≤ 1 2δ q i=1 λ i v i 2 .
Using the result of Lemma 1 and the assumptions introduced thus far, we can define the function φ which maps the measured outputs to the discrete set U to achieve practical stabilization. Proposition 2. Consider a nonlinear system Σ as in (3) satisfying (A0), along with a discrete set U ⊃ V satisfying (A1) and a scalar C V satisfying (6). For a given > 0 assume that γ(C V ) ≤ .

Then by defining φ(y) := arg min

v∈U { v + y } (8)
the control law u = φ(y) globally practically stabilizes Σ with respect to B .

Proof. Suppose that φ(y) = {u i } i∈J for some J ⊂ {1, . . . p} and 0 / ∈ {u i } i∈J . It follows from ( 8) that {u i } i∈J are the closest points to -y and it implies that φ(y), -y ∈ { u i y cos(θ i,y ) | i ∈ J} ⇔ φ(y), y ∈ {-κ i,y u i y | i ∈ J} where θ i,y is the angle between the vectors u i and -y which lies strictly between -π/2 to π/2, and κ i,y := cos(θ i,y ) > 0, accordingly. Otherwise, 0 is (also one of) the nearest element(s) of U to -y, in which case, φ(y), y ∈ {0} {-κ i,y u i y | i ∈ J} or φ(y), y = 0 if φ(y) is a singleton and is given by 0.

Based on this property of φ(y), y , we can now analyze the behaviour of the closed system which is given by ẋ = f (x) + g(x)φ(y) (9) y = h(x). As φ(y) is a non-smooth operator, we consider instead the following differential inclusion ẋ ∈ K f (x) + g(x)φ(y) = f (x) + g(x)K(φ(y)) (10) y = h(x). We note that the solution of ( 9) is also a solution of (10). Therefore, the asymptotic behaviour of all solutions of (10) determines the asymptotic behavior of the solution of (9).

Using the storage function H of the original system, for all solutions of (10) we have that either

(i): φ(y) = {u i } i∈J =: W for some J ⊂ {1, . . . , p}, 0 / ∈ W, so that Ḣ(x) = ∇H(x), ẋ ∈ ∇H(x), f (x) + g(x)K(φ(y)) = ∇H(x), f (x) + y, conv(W) .
Based on the computation of φ(y), y for non-zero φ(y) as before, it follows that y, conv(W) ∈ -κ y,max u i,max y , -κ y,min u i,min y , where u i,max := max w∈W w , u i,min := min w∈W w , κ y,min = cos(θ y,max ) with θ y,max be the largest angle between -y and u i ∈ W and correspondingly, κ y,max = cos(θ y,min ) with θ y,min be the smallest angle between -y and u i ∈ W. Therefore, ≤ -κ y,min u i,min y ; or (ii): φ(y) = {0} {u i } i∈J =: O for some J ⊂ {1, . . . , p}, in which case, following the same arguments as in case (i)

Ḣ(x) ∈ H(x), f (x) + y, conv(O) .
Since {0} is an element of O, y, conv(W) ∈ -κ y,max u i,max y , 0 , where u i,max := max w∈O w . This implies that Ḣ(x) ≤ 0

As H(x(t)) is non-increasing in both cases of (i) and (ii) and since H is proper, all solutions x(t) are bounded. By the LaSalle invariance principle, all of such compact trajectories converge to the largest invariant set M ⊂ R n where h(M ) ⊂ Z where Z := {y ∈ R m | 0 = φ(y)}.

In the invariant set M , φ(y(t)) = 0 for all t and correspondingly, y(t) ≤ C V for each t ≥ 0. By the property of large-time norm-observability of (4), it holds that in the invariant set M where the last inequality is due to the hypotheses of the proposition. Thus, by the LaSalle invariance principle x(t) → M ⊂ B as t → ∞. Due to the properness of H, the above arguments hold for all initial conditions x(0) ∈ R n .

x(t) ≤ γ(C V ) ≤ ∀t ≥ 0,
Example 1. A simple example of U in R 2 , satisfying (A1) is as follows: Here, V p contains all vertices of the convex polytope V. By choosing C V = α, one can check that V ⊂ B α . In the proof of Proposition 2, it is established that the trajectories converge to an invariant set M , such that h(M ) ⊆ V.

U ex := 0, α sin(θex) cos(θex) , α sin(θex+ 2π 3 ) cos(θex+ 2π 3 ) , α sin(θex+ 4π 3 ) cos(θex+ 4π 3 ) = {0, u ex,1 , u ex,2 , u ex,3 } (11) with some θ ex ∈ R and α ∈ (0, ∞).
Remark 3. If the dynamics in system (4) are linear, that is, ẋ = Ax, y = Cx, and the pair (A, C) is observable, then one can quantify γ in (5) using observability Gramian. In particular, if for τ > 0

W τ = τ 0 e A s C Ce As ds then x(t) = W -1 τ t+τ t
e A s C y(s) ds, for each t ≥ 0, and τ > 0, which in particular yields

|x(t)| ≤ W -1 τ τ 0 e A s C ds sup s∈[t,t+τ ) |y(s)|
for each t ≥ 0, and any τ > 0.

Corollary 4. Consider the system Σ as in Proposition 2 with U = λ{-N, -N + 1, . . . , N -1, N } m , λ > 0 being the step size and N a positive integer. Then the control law u = φ(y), where φ is as in (8), globally practically stabilizes Σ with respect to B where > 0 satisfies γ(λ √ m) ≤ .

Proof. The proof follows mutatis mutandis the proof of Proposition 2. The set U satisfies (A1) by taking V = λ{-1, 0, 1} m \ {0}. It is also seen that C V = λ √ m, and by requiring γ(λ √ m) ≤ , all the hypotheses of Proposition 2 hold.

In contrast to the previous example where we used (11) to construct the discrete set U in R 2 , the constant C V in Corollary 4 is less than max ṽ∈ V ṽ . This is due to the choice of the set V in the proof of Corollary 4 that is dense enough such that {y | φ(y) = 0} ⊂ conv(V). From this corollary, one can conclude that two-level quantization with N = 1 suffices to get a global practical stabilization property. This binary control law restricts however the convergence rate of the closed-loop system. It converges to the desired compact ball in a linear fashion and may not be desirable when the initial condition is very far from the origin. The use of higher quantization level (e.g., N >> 1) can provide a better convergence rate when it is initialized within the quantization range.

Before we present our next result, let us recall again the result in Proposition 2. In this proposition, when U is the continuum space of R m , the resulting control law is simply given by u = -y, i.e., it is a unity output feedback law. Using standard result in passive systems theory, the closedloop system will satisfy Ḣ ≤ -y 2 and the application of La-Salle invariance principle with zero-state detectability allows us to conclude that x(t) → 0 asymptotically. As the underlying system is passive, we can in fact stabilize it with any sector-bounded nonlinearity u = -F (y) where F satisfies k 1 y 2 ≤ F (y), y ≤ k 2 y 2 with 0 < k 1 ≤ k 2 . There are a number of reasons for considering such feedback laws rather than the unity output feedback law. For instance, we can attain a prescribed L 2 -gain disturbance attenuation level or we can shape the transient behaviour by adjusting the gains on different domain of y.

In the following proposition, we will consider such sectorbounded output feedback law F (y) which is mapped to our limited control input set U. Proposition 5. Consider the system Σ satisfying (A0), along with a discrete set U ⊃ V satisfying (A1) so that there is a scalar C V such that (6) holds. Let φ be given as in (8); and let F : R m → R m satisfy the sector bound k 1 y 2 ≤ F (y), y ≤ k 2 y 2 , 0 < k 1 < k 2 and F (y) ≤ k 3 y with k 3 > k 1 for all y. Suppose that there exists θ 1,max > 0 such that for all z ∈ R m φ(z) = 0 ⇒ φ(z), -z ≥ φ(z) z cos(θ 1,max ). ( 12) Assume that γ C V /k 1 ≤ , for a given > 0. In addition, if arccos(k 1 /k 3 ) + θ 1,max < π/2, then the control law u = φ(F (y)) globally practically stabilizes Σ with respect to B .

Proof. We prove the theorem by showing that for any given y ∈ R m , we have either φ(F (y)), y ∈ {-κ i,y u i y | i ∈ J} for some J ⊂ {1, . . . , p} with κ i,y > 0 and {u i } i∈J be the nearest element(s) of U to -F (y); or when 0 ∈ φ(F (y)) φ(F (y)), y ∈ {0} {-κ i,y u i y | i ∈ J} for some J ⊂ {1, . . . , p}, or φ(F (y)), y = 0 if φ(F (y)) is a singleton and is given by 0. The rest of the proof follows similarly to that of Proposition 2.

Similar to the arguments used in the proof of that proposition, suppose that φ(F (y)) = {u i } i∈J for some J and 0 / ∈ φ(F (y)). It follows from ( 8) that {u i } i∈J are the closest points to -F (y) which implies that φ(F (y)), -F (y) ∈ { u i F (y) cos(θ i,1 ) | i ∈ J}, where θ i,1 is the angle between the vectors u i and -F (y). By the hypothesis of the proposition, |θ i,1 | ≤ θ 1,max for any i. On the other hand, -F (y), -y = F (y) y cos(θ 2 ), where θ 2 is the angle between the vectors F (y) and y. Since k 1 y 2 ≤ F (y), y and F (y) ≤ k 3 y , the maximum angle θ 2 is given by θ 2,max = arccos(k 1 /k 3 ). Hence the inner product between the vectors φ(F (y)) and -y satisfies φ(F (y)), -y ∈ { u i y cos(θ i,3

) | i ∈ J}, where |θ i,3 | ≤ |θ i,1 | + |θ 2 | ≤ θ 1,max + θ 2,max < π/2. Therefore φ(F (y)), y ≤ {-κ y u i y | i ∈ J} where κ y = cos θ 1,max + arccos(k 1 /k 3 ) > 0.
Following the same line of arguments as in the proof of Proposition 2, the solutions x(t) of the differential inclusion (10) converge to the invariant set M , in which φ(F (y(t))) = 0 for all t. Hence in M , F (y

(t)) ≤ C V for all t. Since k 1 v 2 ≤ F (v), v ≤ F (v) v holds for all v ∈ R m , it follows that y(t) ≤ C V
k1 for all t in M . By the property of large-time norm-observability of (4), it holds that in the invariant set M x(t) ≤ γ(C V /k 1 ) ≤ ∀t, where the last inequality is due to the hypotheses of the proposition. Thus, by the LaSalle invariance principle x(t) → M ⊂ B as t → ∞.

EXAMPLE AND SIMULATION RESULTS

In this section, we will apply our main results to an academic example and illustrate the behaviour of the closed-loop system through a numerical simulation.

Example 2. Consider the following nonlinear system Σ ex :

           ẋ =   -x 2 + x 2 3 x 1 + x 2 3 -x 1 x 3 -x 2 x 3   + 1 0 0 0 0 1 u y = x 1 x 3 (13)
where

x := [x 1 x 2 x 3 ] ∈ R 3 and y := [y 1 y 2 ] , u := [u 1 u 2 ] ∈ R 2 .
It can be checked that by using the proper storage function H(x) = 1 2 x x, the system Σ ex is passive. Indeed, a straightforward computation gives us Ḣ = y, u .

We will now show that Σ ex satisfies the large-time norm observability condition. As the bound on x 1 and x 3 for the large-time norm observability can directly be obtained from the output y, we need to compute the bound on x 2 . If we consider the sub-system of [ x1 x2 ] with x 1 as its output (and is equal to y 1 ), it is a linear system with

A = 0 -1 1 0 , B = [ 1 0 0 1 ], C = [1 0] and its input is [ x 2 3 x 2 3
] . Thus, as (A, C) is observable, we can choose the following observability Gramian

W π = π 0 e A s C Ce As ds = π 2 1 0 0 1 , whose inverse is simply given by W -1 π = 2 π I 2 and W -1 π = 2 π . Then for any t > 0 x 1 (t) x 2 (t) = W -1 π t+π t e A s C x 1 (s) -L * x 2 3
x 2 3 (s) ds, where * denotes the convolution operation and L is the convolution matrix kernel given by L(t) = Ce At . Since e At = 1 for all t, it follows then that x 1 (t)

x 2 (t) . In other words, the function γ in (5) is given by γ(s) = 4(s + s 2 ). Note that when the output of Σ ex is only x 1 or x 3 , it is not large-time norm observable and it is even not zero-state observable/detectable.

We will now use the result in Proposition 2 for practical stabilization of Σ ex . We choose the control set to be U ex given in (11), and the desired stability margin to be = 0.5. Then, based on the function γ computed for the system Σ ex , we get γ(C V ) < 0.5 if C V ∈ 0, -1 2 + 1 4 √ 6 . By letting θ ex = 0 and α = 0.1 in (11), it is seen that Σ ex is globally practically stable with respect to B , with = 0.5, as confirmed by simulations reported in Figure 2.

CONCLUSIONS AND FURTHER RESEARCH

We have considered stabilization of continuous-time passive nonlinear systems, under appropriate observability assumption, using output feedback where the control input can switch between finitely many values. Our results provide a lower bound on the number of control elements, and conditions on their configuration in input space, which guarantee practical stability. We are currently investigating an algorithmic procedure for designing the finite control actions with minimal cardinality such that the value of the constant C V is minimized. Questions related to improving the convergence rate with more (than necessary) control elements also require further research.

Fig. 1 .

 1 Fig. 1. Illustration of the region of the solution of φ(y) for the discrete set Uex as in (11) with θex = 0 and α = 1.

  For this example, (A1) holds by taking V := U \ {0}. Following the proof of Lemma 1, we have {y | φ(y) = 0} = V := conv V p where V p := α

Fig. 2 .

 2 Fig. 2. Simulation results of Σex using nearest neighbor control approach with discrete input set Uex as in (11) and fixed parameters θex = 0 and α = 0.1. The evolution of control actions is plotted with shorter time frame to show the switch before the state x and the output y finally enter the desired forward invariant set.

  Since by the definition of y, x 1 [t,t+π] = y 1 [t,t+π] ≤ y [t,t+π] and x 3 [t,t+π] = y 2 [t,t+π] ≤ y [t,t+π] , it follows from the inequality above that x [t,t+π] ≤ 4 y [t,t+π] + y 2 [t,t+π]

	≤ W -1 π ≤ 2 π π y 1 [t,t+π] + t+π e A s C t √ 2 y 2	x 1 (s) -L * 2 [t,t+π]	x 2 3 x 2 3	(s) ds
	≤ 2 y [t,t+π] + 4 y 2 [t,t+π] .