G Granato

H Zidani

LEVEL-SET APPROACH FOR REACHABILITY ANALYSIS OF HYBRID SYSTEMS UNDER LAG CONSTRAINTS *

Keywords: Optimal control, Quasi-variational Hamilton-Jacobi equation, Hybrid systems, Reachability analysis AMS subject classifications. 49LXX, 34K35, 34A38, 65M12

This study aims at characterizing a reachable set of a hybrid dynamical system with a lag constraint in the switch control. The setting does not consider any controllability assumptions and uses a level-set approach. The approach consists in the introduction of an adequate hybrid optimal control problem with lag constraints on the switch control whose value function allows a characterization of the reachable set. The value function is in turn characterized by a system of quasi-variational inequalities (SQVI). We prove a comparison principle for the SQVI which shows uniqueness of its solution. A class of numerical finite difference scheme for solving the system of inequalities is proposed and the convergence of the numerical solution towards the value function is studied using the comparison principle. Some numerical examples illustrating the method are presented. Our study is motivated by an industrial application. We are interested in the maximum range of hybrid vehicles.

1. Introduction. This paper deals with the characterization of a reachable set of a hybrid dynamical system with a lag constraint in the switch control. The approach consists in the introduction of an adequate hybrid optimal control problem with lag constraints on the switch control whose value function allows a characterization of the reachable set.

The term hybrid system refers to a general framework that can be used to model a large class of systems. Broadly speaking, they arise whenever a collection of continuous-and discrete-time dynamics are put together in a single model. In that sense, the discrete dynamics may dictate switching between the continuous dynamics, jumps in the system trajectory or both. Moreover, they can contain specificities, as for instance, autonomous jumps and/or switches, time delay between discrete decisions, switching/jumping costs. This work considers a particular class of hybrid systems where only switching between continuous dynamics are operated by the discrete logic, with no jumps in the trajectory, and with no switching costs. In addition, switch decisions are constrained to be separated in time by a non-zero interval, fact which is referred to as switching lag.

Before referring to the reachability problem in the hybrid setting, the main ideas are introduced in the non-hybrid framework. Given a time t > 0, a closed target set C and a closed admissible set K, consider a controlled dynamical system ẏ(s) = f (s, y(s), u(s)), a.e. s ∈ [0, t],

(

where f : IR + × IR d × U → IR d and u : IR + → U is a measurable function. The reachable set R C (t) is defined as the set of all positions x for which there exists a trajectory that starts in C, stays inside K on [0, t] and arrives in x at time t: R C (t) := {x | ∃(y, u) satisfies (1.1), with y(0) ∈ C and y(s) ∈ K on [0, t], and y(t) = x}.

It is a known fact that the reachable set can be characterized by the negative region of the value function of an optimal control problem. For this, following the idea introduced by Osher and Sethian [START_REF] Osher | Fronts propagating with curvature dependent speed: Algorithms based on hamilton-jacobi formulations[END_REF], one can consider the control problem defined by: v(x, t) := inf{v 0 (y(0)) | (y, u) satisfies (1.1), with y(t) = x and y(s) ∈ K on [0, t]}, (1.2) where v 0 is a Lipschitz continuous function satisfying v 0 (x) ≤ 0 ⇐⇒ x ∈ C (for instance, v 0 can be the signed distance d C to C). Under classical assumptions on the vector-field f , one can prove that the reachable set is given by

R C (t) = {x ∈ K, v(x, t) ≤ 0}.
Moreover, when K is equal to IR d , the value function has been shown to be the unique viscosity solution of a Hamilton-Jacobi-Bellman (HJB) equation [START_REF] Bardi | Optimal control and viscosity solutions of Hamilton-Jacobi-Bellman equations[END_REF] (for every t > 0):

∂ t v + sup u∈U {-f (s, x, u) • ∇v} = 0 on IR d × (0, t],
with the initial condition v(x, 0) = v 0 (x).

When the set K is a subset of IR d (K = IR d ), the characterization of v by means of a HJB equation becomes a more delicate matter. However, it was pointed out in [START_REF] Bokanowski | Reachability and minimal times for state constrained nonlinear problems without any controllability assumption[END_REF] that in case of state-constraints, it is still possible to characterize the reachable set by an adequate control problem involving a supremum cost (see Section 2.3 for more details).

In this paper, we are interested in the extension of the reachability framework to some class of control problems of hybrid systems.

Let us recall that a hybrid dynamical system is a collection of controlled continuoustime processes selected through a high-level discrete control logic. A general framework for the (optimal) control hybrid dynamical systems was introduced in [START_REF] Branicky | A unified framework for hybrid control: Model and optimal control theory[END_REF]. Several papers deal with the optimal control problem of hybrid systems, let us just mention here the papers [START_REF] Arutyunov | Necessary conditions for impulsive nonlinear optimal control problems without a priori normality assumptions[END_REF][START_REF] Caines | A maximum principle for hybrid optimal control problems with pathwise state constraints[END_REF][START_REF] Garavello | Hybrid Necessary Principle[END_REF][START_REF] Sussmann | A maximum principle for hybrid optimal control problems[END_REF] where the optimality conditions in the form of Pontryagin' principle are studied and [START_REF] Dharmatti | Hybrid control systems and viscosity solutions[END_REF][START_REF] Dolcetta | Optimal switching for ordinary differential equations[END_REF][START_REF] Zhang | Optimal control of hybrid systems and a systems of quasi-variational inequalities[END_REF] where the HJB approach is analyzed.

A feature of the hybrid system used in our work is a time lag between two consecutive switching decisions. From the mathematical viewpoint this removes the particularities linked to Zeno-like phenomena [START_REF] Yu | Observability normal forms for a class of switched systems with zeno phenomena[END_REF]. Indeed, the collection of state spaces is divided in subsets labeled in three categories according to whether they characterize discrete decisions as optional, required (autonomous) or forbidden. Landing conditions ensure that whatever the region of the state space the state vector "lands" after a switch no other switch is possible by requiring a positive distance (in the Hausdorff sense) between the landing sets and the optional/autonomous switch sets. In the other hand, when allowed to switch freely without any costs, when no time interval is imposed between discrete transitions, a controller with a possibly infinite number of instantaneous switches may become admissible. Switch costs can be introduced in order to rule out this kind of strategy by the controller as it becomes over-expensive to switch to a particular mode using superfluous transitions. However, such costs do not make sense in the level-set approach used in this paper.

Diffusion processes with impulse controls including switch lags are studied in [START_REF] Bruder | Impulse control problem on finite horizon with execution delay[END_REF], where it is considered the idea of introducing a state variable to keep track of the time since the last discrete control decision. There, in addition, discrete decisions also suffer from a time delay before they can manifest in the continuous-time process. In that case, one has the possibility of scheduling discrete orders whenever the time for a decision to take place may be longer than that of deciding again. Then, the analysis also includes keeping record of the nature of this scheduled orders. This work inspired the idea of a state variable locking possible transitions used here.

To study the reachability sets for our system, we follow the level set approach and adapt the ideas developed in [START_REF] Bokanowski | Reachability and minimal times for state constrained nonlinear problems without any controllability assumption[END_REF] to hybrid systems by proposing a suitable control problem which allows us to handle in a convenient way the state constraints and the decision lag. Here the main difficulty is to characterize the value function associated to the control problem. It turns out that this value function satisfies a quasi-variational HJB inequality system (in the viscosity sense). A comparison principle is derived for this system. This paper is organized as follows: Section 2 states the associated hybrid optimal control problem and defines the reachable set and the value function. Section 3 presents the main results of the paper: it establishes a dynamic programming principle for the value function, shows that the value function is Lipschitz continuous and a solution of an system of quasi-variational inequalities (SQVI). It follows with the proof of a comparison principle for the SQVI that ensures uniqueness of its solution. Section 4 presents a numerical analysis of the SQVI. It shows the convergence of a class of numerical schemes for the computation of the value function and illustrates the convergence of a particular discretization scheme for a simple instance inspired by the vehicle applications.

Motivation and Problem Settings.

2.1. Hybrid Vehicles. A hybrid vehicle is a vehicle with two different sources of energy. The first one is a fuel tank and provides power through an internal combustion engine (ICE). The second one is a battery and provides power through an electric motor. Both the vehicle's energy sources are considered to have normalized energetic capacities -thus valued between 0 and 1.

The controls available to the controller are the ICE state -on or off -and the power produced in the ICE. The power produced in the ICE is a non-negative piecewise continuous function of time. The ICE's state is controlled by a discrete sequence of switching orders decided and executed at discrete times. The model assumes that the vehicle must stop whenever there is no charge left in the battery. Additionally, a feature of this model is a time interval δ > 0 imposed between two consecutive decisions times. From the physical viewpoint, this assumption incorporates the fact that frequent switching of the ICE is undesirable in order to avoid mechanical wear off and acoustic nuisance for the driver. In this setting, the optimal controller seeks to best control the power of the ICE and its state to maximize the vehicle's autonomy, i.e. drive to the furthest point possible. The objective of finding the vehicle autonomy therefore translates in reaching the furthest point away from the vehicle geographic starting point where the battery is depleted for the first time.

Hybrid Dynamical

System. Hybrid systems have some supervision logic that intervenes punctually between two or more continuous functions. The main elements of the class of hybrid dynamical systems considered in this work are a discrete state q valued in a discrete set Q, and a family of continuous dynamics (vector fields) f and a continuous state space X = IR d .

More precisely, the continuous state variable is denoted by y and it is valued in the state space X. The discrete variable is q ∈ Q = {0, 1, • • • , d q }, where d q is the number of possible dynamics that can operate the system.

The continuous control is a measurable function u : [0, ∞[→ IR m valued in a set that depends on the discrete state U (q). The discrete control is a sequence of switching decisions

w = {(w 1 , s 1 ), • • • , (w i , s i ), (w i+1 , s i+1 ), • • • }, (2.1)
where the s i are switching times and the w i are switching decisions. Switching times can happen at any time, therefore s i ∈ [0, ∞[. Switching decisions are valued in a discrete set W (q) ⊂ Q that depend on the discrete state, therefore w i ∈ W (q). Set W (q) is the set of available orders when in discrete state q. The sequence of discrete switching decisions {w i } i>0 (designating the new mode of operation) is associated with a sequence of switching times {s i } i>0 where each decision w i is exerted at time s i . The case where no switch can occur corresponds to a single discrete input (w 1 , s 1 ) where s 1 = 0 and w 1 ∈ Q (this decision will have no effect on the controlled system as it is considered backward in time, see (2.5)). Denote by A the space of hybrid controls a = (u, (w i , s i ) i ).

The lag condition between switches is included as a control constraint that imposes two switch orders to be separated by a time interval of δ > 0, i.e.,

s i+1 -s i ≥ δ.
We precise the class of admissible controls A ⊂ A in the following definition: Definition 2.1. For a fixed time horizon T ≥ 0 a hybrid control a = (u, (w i , s i ) i ) ∈ A is said to be admissible if the continuous control satisfies u(s) ∈ U (q(s)) and the discrete control sequence w = {w i , s i } i≥1 has increasing decision times

s 1 ≤ s 2 ≤ • • • ≤ s i ≤ s i+1 ≤ • • • ≤ T, (2.2a
)

admissible decisions ∀i > 0, w i ∈ W (q(s i )) ⊂ Q, (2.2b)
and verifies a decision lag

s i+1 -s i ≥ δ, (2.2c)
where δ > 0.

Let us point out that the above definition includes the case where no switch occurs during the interval time (0, T ], i.e. the sequence of switching times is reduced to one single element s 1 = 0.

An important consequence in the definition of admissible control is the finiteness of the number of switch orders: Proposition 2.2. Fix T ≥ 0. Let a ∈ A be an admissible hybrid control. Then, the discrete control sequence has at most N = T /δ switch decisions.

Proof. The proof is trivial in a finite-time setting with imposed delay between switching orders.

In this setting, given a discrete state q(s) = q, a final state x, a switching sequence (w i , s i ) and a continuous control u. The system evolves according to ẏ(s) = f (s, y(s), u(s), q(s)), for a.e. s ∈ [0, t], y(t) = x.

(2.3) When q(s) = q i , the vector field corresponding to the discrete state q i is active and dictates the evolution of the continuous state. At some isolated times {s i } i>0 , given by the discrete switching control sequence, the discrete dynamics g : Q 2 → Q is activated (notice that the system is backward in time)

q i-1 = g(w i , q i ) (2.4)
and the continuous state follows another vector field f (•, •, •, q i-1 ). In the considered system, the discrete decisions only switch the continuous dynamics and introduce no discontinuities on the trajectory.

In more general frameworks, we can also include jumps in the continuous state vector that can be used to model an instant change in the value of the state following a discrete decisions [START_REF] Bensoussan | Hybrid control and dynamic programming[END_REF][START_REF] Branicky | A unified framework for hybrid control: Model and optimal control theory[END_REF].

Assume controlled continuous dynamics f , the lag δ and the set of control values U (q) satisfy the following assumptions: (H0) Through all the paper the lag interval δ > 0 is fixed. For every q ∈ Q, U (q) is a compact set of IR m (m ≥ 1). (H1) There exists L f > 0 such that, for all s ≥ 0, y, y ∈ X, q ∈ Q and u ∈ U (q), f (s, y, u, q) -f (s, y , u, q) ≤ L f y -y , f (s, y, u, q) ≤ L f . (H2) For all q ∈ Q, f (•, •, •, q) : [0, ∞[×X × IR m → X is continuous. (H3) For all s ≥ 0, y ∈ X and q ∈ Q, f (s, y, U (q), q) is a convex subset of X.

Assumption (H0),(H1) ensures that a trajectory exists and that it is unique. Assumption (H2) is used to prove the Lipschitz continuity of the value function and (H3) is needed in order to observe the compactness of the trajectory space. Now we introduce the hybrid trajectory. Fix a time horizon T > 0 and let 0 < t < T . Given a hybrid control a = (u, (w i , s i ) i ) ∈ A with at most N switch orders and given x ∈ X, q ∈ Q, the hybrid dynamical system is ẏ(s) = f (s, y(s), u(s), q i ), for s ∈ (s i-1 , s i ), and y(t) = x, (2.5a) q(s) = q i , for s ∈ [s i-1 , s i ), and q(t) = q, (2.5b)

q i-1 = g(w i , q i ), i = 1, • • • , N. (2.5c)
Denote the solutions of (2.5a)-(2.5c) with final conditions x, q by y x,q;t and q x,q;t . As pointed out, not all discrete control sequences are admissible. Only admissible control sequences engender admissible trajectories. Thus, given t > 0, x ∈ X and q ∈ Q, the admissible trajectory set Y x,q [0,t] is defined as

Y x,q [0,t] = {y(•) | a ∈ A and y x,q;t solution of (2.5) } (2.6)
A consequence of proposition 2.2 and the above definition is the finiteness of the number of discrete decisions in any admissible trajectory.

The hybrid control admissibility condition formulated as in conditions (2.2) is not well adapted to a dynamic programming principle formulation, needed later on. In order to include the admissibility condition in the optimal control problem in a more suitable form, we associate to each discrete control (w i , s i ) i a new state variable π.

Recall that the decision lag conditions implies that new switch orders are not available up to a time δ since the last switch. The new variable is constructed such that at a given time s ∈ [0, t], the value of π(s) measures the time since the last switch. The idea is to impose constraints on this new state variable and treat them more easily in the dynamic programming principle. Thus, if π(s) < δ all switch decisions are blocked and if, conversely, π(s) ≥ δ the system is free to switch. For that reason, this variable can be seen as a switch lock. More precisely, given t > 0, s ∈ [0, t] and a discrete control {w i , s i } i>0 , the switch lock dynamics is defined by

π(s) = δ + s if s < s 1 inf si≤s s -s i if s ≥ s 1 (2.7)
Indeed, once the discrete control is given, the trajectory π(•) can be determined. Proceeding with the idea of adapting the admissibility condition in order to manipulate it in a dynamic programming principle, we consider π(t) = p, with p ∈ [0, T ] the final value of the switch lock variable trajectory and impose the lag condition under the form π(s - i ) ≥ δ for all s i , where s - i denotes the limit to the left at the switching times s i (notice that π(s + i ) = 0 by construction). Note that when a switch can occur, the final value of the switch lock p should be in [0, t), while when no switch can occur, p can be considered as any value in [t, T ).

Then, since these conditions suffice to define an admissible discrete control set, while optimizing with respect to admissible functions, one needs only look within the set of hybrid controls that engenders a trajectory π(•) with the appropriate structure. In other words, given t > 0, x ∈ X, q ∈ Q and p ∈ [0, T ), define a admissible trajectory set S x,q,p [0,t] as

S x,q,p [0,t] = {y(•) | a = (u, {w i , s i } N i=1 ) ∈ A, y x,q,p;t solution of (2.5a)-(2.5c), π(•) solution of (2.7), π(t) = p, π(s - i ) ≥ δ, i = 1, • • • , N }. (2.8)
The next lemma states a relation between sets Y and S: Lemma 2.3. Set T > 0 is a final horizon. Following the above definitions, for every t ∈ [0, T ], sets (2.6) and (2.8) satisfy

Y x,q [0,t] = p∈[0,T ) S x,q,p [0,t] .
Proof. The equivalence between Y x,q [0,t] and p∈[0,T ) S x,q,p [0,t] is obtained by construction.

In the following of the paper, whenever we wish to call attention to the fact that the final conditions of (2.5a), (2.5c) and (2.7) are fixed, we denote their solutions respectively by y x,q,p;t , q x,q,p;t , π x,q,p;t .

Reachability of Hybrid Dynamical Systems and Optimal Control

Problem. Let C ⊂ X be the set of allowed initial states, i.e. the set of states from which the system (2.5a)-(2.5c) is allowed to start. Moreover, define a compact set K ⊂ X as the hybrid system admissible set, i.e., a set inside which the state must remain.

In the sequel C and K are assumed to be closed nonempty sets. Consider a finite horizon T > 0 and let 0 < t < T . Define the reachable set as:

R C (t) = {x | ∃q ∈ Q, y ∈ Y x,q [0,t] , y(0) ∈ C, and y(θ) ∈ K ∀θ ∈ [0, t]} = {x | ∃(q, p) ∈ Q×[0, T ), y ∈ S x,q,p
[0,t] , y(0) ∈ C, and y(θ) ∈ K ∀θ ∈ [0, t]}. The reachable set R C (t) contains the values of y x,q;t (t) = x, regardless of the final discrete state, for all admissible trajectories -i.e., trajectories obtained through an admissible hybrid control -starting within the set of possible initial states C, that never leave set K.

The following proposition ensures that the space of admissible trajectories is a compact set.

Proposition 2.4. Given T > 0 and assume (H1),(H2),(H3). Then, the admissible trajectory set Y x,q [0,T ] is a compact set in C([0, T ]) endowed with the W 1,1 -topology.

Proof. Fix q ∈ Q and 0 ≤ s < t ≤ T . Consider a bounded admissible continuous control sequence u n ∈ L 1 ([s, t]). Since u n is bounded, there exists a subsequence u nj such that u nj u in L 1 ([s, t]). Invoking (H1),(H2), we have

y un = y n y in W 1,1 ([s, t]). Since W 1,1 ([s, t]) is compactly embedded in C 0 ([s, t]
), we get the strong convergence of the solution y n → y in C 0 ([s, t]). Hypothesis (H3) guarantees that the limit function y is a solution of (2.5a). Because all controls u n and the limit control u are admissible, y is an admissible solution.

So far, the proof shows that the limit trajectory is admissible when q is hold constant.

The goal of the following argument is to extend the previous reasoning to pieces of any trajectory, since q will eventually be constant outside switching times, due the finiteness of switches.

Consider a sequence of admissible discrete control sequences (w) n where the number of switching orders, 0 ≤ k n ≤ T /δ may depend on n. Since each term of this sequence has a (first) discrete component and is bounded on the (second) continuous component, then, as n → ∞ there exists a subsequence (w) n l and λ > 0 such that q n l = q for all l > λ. This implies k n → k. As the number of switches is constant from the λ th term and s, t are arbitrary, one can obtain, using the limit discrete control sequence w, the time intervals [s i-1 , s i ] over which q i is constant and the argument in the first paragraph of the proof. Because the trajectory is continuous and admissible on all time intervals

[s i-1 , s i ], i = 1, • • • , I, it is admissible on [0, T ]. This completes the proof.
Remark 2.1. The arguments presented in the above proof can be slightly modified to show that the admissible trajectory set with fixed final p, S x,q,p [0,T ] is compact. Also in a similar way, the proof can be adapted to show that the reachable R C (t) is closed for any t ≥ 0. Indeed, by the closedness of set C, a sequence of initial conditions (y 0 ) n ∈ C, associated with admissible trajectories y n ∈ Y x,q [0,T ] , converges to y 0 ∈ C which is also the initial condition for the limiting trajectory y n → y.

In order to characterize the reachable sets R C (•) this paper follows the classic level-set approach [START_REF] Osher | Fronts propagating with curvature dependent speed: Algorithms based on hamilton-jacobi formulations[END_REF]. The idea is to describe (2.9) as the negative region of a function v. It is well known that the function v can be defined as the value function of some optimal control problem. In the case of system (2.5a)-(2.5c), v happens to be the value function of a hybrid optimal control problem.

Consider a bounded Lipschitz continuous function

φ : X → IR such that φ(x) ≤ 0 ⇔ x ∈ C.
(2.9) Such a function always exists -for instance, the function

φ(x) := max(min(d C (x), L K ), -L K ),
where d C is the signed distance function from the set C, and L K > 0 a positive constant. Define a bounded Lipschitz continuous function γ : X → IR to be

γ(x) ≤ 0 ⇔ x ∈ K. (2.10)
Such a function always exist as far as K is a closed set. In the sequel, we consider L K > 0 a positive constant such that:

|γ(x)| ≤ L K and |φ(x)| ≤ L K , ∀x ∈ X.
Then, for a given t ≥ 0, (x, q, p) ∈ X × Q × [0, T ) and y ∈ S x,q,p [0,t] , define a total cost function to be

J(x, q, p, t; y) = φ(y(0)) max θ∈[0,t] γ(y(θ))
and then, the optimal value : v(x, q, p, t) = inf y∈S x,q,p [0,t] J(x, q, p, t; y).

(2.11)

Observe that (2.11) is bounded thanks to the constructions (2.9) and (2.10). The idea in place is that one needs only to look at the sign of v to obtain information about the reachable set. Therefore, the bound L K removes the necessity of dealing with an unbounded value function besides providing a natural bound for numerical computations.

3. Main Results. The next proposition certifies that (2.9) is indeed a level-set of (2.11).

Proposition 3.1. Assume (H1)-(H3). Define Lipschitz continuous functions φ and γ by (2.9) and (2.10) respectively. Define the value function v by (2.11). Then, for t ≥ 0, the reachable set is given by

R C (t) = {x | ∃(q, p) ∈ Q × [0, T ), v(x, q, p, t) ≤ 0} (3.1)
Proof. Assume y x,q,p;t (t) ∈ R C (t). Then, by definition, there exists (q, p) ∈ Q × [0, T ) and an admissible trajectory such that y x,q,p;t (θ) ∈ K for all time and y x,q,p;t (0) ∈ C. This implies that max θ∈[0,t] (γ(y x,q,p;t (θ))) ≤ 0 and φ(y x,q,p;t (0)) ≤ 0. It follows that v(x, q, p, t) ≤ J(x, q, p, t; y) ≤ 0.

Conversely, assume v(x, q, p, t) ≤ 0. For any optimal trajectory ŷ (which is admissible thanks to Proposition 2.4) v(x, q, p, t) = J(x, q, p, t; ŷ) ≤ 0. Since the maximum of the two quantities is non positive only if they are both non positive one can draw the desired conclusion. Proposition (3.1) states that one can draw information about R C (•) by computing v. In the sequel, it is shown that the value function defined in (2.11) is the unique (viscosity) solution of a quasi-variational inequalities' system. The first step is to state a dynamic programming principle for (2.11).

First, we present some preliminary notation. Given T > 0, set Ω = X × Q × (0, T ) × (0, T ] and denote its closure by Ω. For a fixed p 0 ∈ [0, T ), define Ω| p0 = X × Q × {p 0 } × (0, T ] and denote Ω| p0 the closure of Ω| p0 . Define

V(Ω) := {v | v : Ω → IR, v bounded }, (3.2)
V(Ω| p0 ) := {v | v : Ω| p0 → IR, v bounded }, for p 0 ∈ [0, T ). (3.3)
For v ∈ V(Ω), denote its upper and lower envelope at point (x, q, p, t) ∈ Ω respectively as v * and v * :

v * (x, q, p, t) = lim sup xn→x qn→q pn→p tn→t v(x n , q n , p n , t n ) (3.4) v * (x, q, p, t) = lim inf xn→x qn→q pn→p tn→t v(x n , q n , p n , t n ) (3.5)
In the case where p 0 ∈ [0, T ) is fixed and v ∈ V(Ω| p0 ), the upper and lower envelopes of v are also given by (3.4), (3.5) with p n = p 0 for all n. Now, fix p = 0 and define the non-local switch operators M, M + , M -: V(Ω| 0 ) → V(Ω| 0 ) to be (M v)(x, q, 0, t) = inf w∈W (q) p ≥δ v(x, g(w, q), p , t)

(M + v)(x, q, 0, t) = inf w∈W (q) p ≥δ v * (x, g(w, q), p , t) (M -v)(x, q, 0, t) = inf w∈W (q) p ≥δ v * (x, g(w, q), p , t)
The action of these operators on the value function represents a switch that respects the lag constraint. They operate whenever a switch is activated, which is equivalent to the condition p = 0. Therefore, they are defined only for a fixed p = 0. In the following, denote BU SC( Ω) and BU SC( Ω) the set of bounded upper semicontinuous and bounded lower semi-continuous functions valued in Ω, respectively. Let us recall here some classical properties of operators M, M + and M -(adapted from [START_REF] Zhang | Optimal control of hybrid systems and a systems of quasi-variational inequalities[END_REF]):

Lemma 3.2. Let v ∈ V( Ω). Then M + v * ∈ BU SC( Ω) and M -v * ∈ BLSC( Ω). Moreover (M v) * ≤ M + v * and (M v) * ≥ M -v * .
Proof. Fix q ∈ Q, p = 0 and > 0. Let w * ∈ W (x, q) and p * > 0 be such that for all x ∈ X and t ∈ (0, T ), (M + v * )(x, q, p, t) ≥ v * (x, g(w * , q), p * , t) -. Consider sequences x n → x and t n → t. Then

M + v * (x, q, 0, t) ≥ v * (x, g(w * , q), p * , t) - ≥ lim sup xn→x tn→t v * (x n , g(w * , q), p * , t n ) - ≥ lim sup xn→x tn→t inf w∈W (xn,q),p ≥δ v * (x n , g(w, q), p , t n ) - = lim sup xn→x tn→t (M + v * )(x n , q, 0, t n ) -.
Notice that q and p are held constant throughout the inequalities and thus, the limsup of the jump operator considering only sequences x n → x and t n → t corresponds to its envelope at the limit point. Then, by the arbitrariness of , this proves the upper semi-continuity of M + v * . The lower semi-continuity of M -v * can be obtained in a similar fashion. Now, observe that M v ≤ M + v * . Taking the upper envelope of each side, one obtains:

(M v) * ≤ (M + v * ) * = M + v * .

By the same kind of reasoning

M v ≥ M -v * and (M v) * ≥ (M -v * ) * = M -v * .
The next proposition is the dynamic programming principle verified by (2.11): Proposition 3.3. The value function (2.11) satisfies the following dynamic programming principle:

(i) For t=0, v(x, q, p, 0) = max(φ(x), γ(x)), ∀(x, q, p)

∈ X × Q × [0, T ), (3.6)
(ii) For p = 0, v(x, q, 0, t) = (M v)(x, q, 0, t), (x, q, t)

∈ X × Q × [0, T ], (3.7)
(iii) For (x, q, p, t) ∈ Ω, define the non-intervention zone as Σ = (0, p ∧ t). Then, for h ∈ Σ,

v(x, q, p, t) = inf S x,q,p [t-h,t] v(y x,q,p;h (t -h), q, p -h, t -h) max θ∈[t-h,t]
γ(y x,q,p;h (θ)) (ii): " ≤ ". Let (x, q, t) ∈ X ×Q×[0, T ] and p = 0. Consider an admissible hybrid control a = (u(•), {w i , s i } N i=1 ) and construct a new control a = (u(•), {w i , s i } N -1 i=1 ) with associated trajectory y a , where u = u, w i = w i and s i = s i for i = 1, • • • , N -1 and s N = t, w N = w . Then, one obtains: v(x, q, 0, t) ≤ J(x, q, 0, t; y a ) = J(x, g(w , q), p , t; y a ), where the controller must respect the condition p ≥ δ for it to be admissible. Since a is arbitrary, then we get: v(x, q, 0, t) ≤ inf y a ∈S (x,g(w ,q),p ) [0,t] J(x, g(w , q), p , t; y a ) = v(x, g(w , q), p , t)

for every p ≥ δ, w ∈ W (q). This yields to v(x, q, 0, t) ≤ inf w ∈W (q) p ≥δ v(x, g(w , q), p , t)

= (M v)(x, q, p, t).

" ≥ ". For (x, q) ∈ X × Q and p = 0, for > 0, there exists an admissible control a , such that v(x, q, 0, t) + ≥ J(x, q, 0, t; y a ).

Using the same hybrid control constructions as in the "≤" case, one obtains J(x, q, 0, t; y a ) = J(x, g(w , q), p , t; y ā ) ≥ v(x, g(w , q), p , t)

≥ inf w ∈W (q)
p ≥δ v(x, g(w , q), p , t) = (M v)(x, q, 0, t).

Relation (3.7) is obtained by the arbitrariness of .

(iii): " ≤ ". For (x, q, p, t) ∈ Ω and 0 < h ≤ p ∧ s, (2.11) yields v(x, q, p, t) ≤ max φ(y x,q,p;t (0)) max θ∈[0,t-h] γ(y x,q,p;t (θ)) , max

θ∈[t-h,t]
γ(y x,q,p;t (θ)) , (3.9) for any y ∈ S x,q,p [0,t] . By the choice of h, there is no switching between times t -h and t. Write the admissible control a = (u, w) as a 0 = (u 0 , w) and a 1 = (u 1 , w) with

u 0 (s) = u(s), s ∈ [0, t -h], u 1 (s) = u(s), s ∈ (t -h, t].
Since a is admissible, both controls a 0 , a 1 are also admissible. Denote the trajectory associated with controls a, a 0 , a 1 respectively by y a , y 0 , y 1 . Then, y a ∈ S x,q,p [0,t] and by continuity of the trajectory we achieve the following decomposition:

y 1 ∈ S x,q,p [t-h,t] , y 0 ∈ S y 1 (t-h),q,p-h [0,t-h]
.

The above decomposition together with inequality (3.9) yields v(x, q, p, t) ≤ max φ(y 0 (0)) max

θ∈[0,s-h] γ(y 0 (θ)) , max θ∈[t-h,t] γ(y 1 (θ)) , (3.10)
And one concludes after minimizing with respect to the trajectories associated with a 0 and a 1 .

The " ≥ " part uses an -optimal controller and the same decomposition to obtain the inverse inequality of (3.10), allowing to conclude by the arbitrariness of . This is possible because there is no switching between t -h and t.

A direct consequence of proposition 3.3 is the Lipschitz continuity of the value function, stated in the next proposition: Proposition 3.4. Assume (H1)-(H2). Define Lipschitz continuous functions φ and γ by (2.9) and (2.10), with Lipschitz constants L φ and L γ respectively. Then, for any T > 0, the value function defined in (2.11)

is Lipschitz continuous on X × Q × [0, T ) × [0, T ].
Proof. Fix t ≥ 0, x, x ∈ X, q ∈ Q, p > 0. Then, using max(A, B) -max(C, D) ≤ max(A -B, C -D), one obtains, |v(x, q, p, t) -v(x , q, p, t)| ≤ max |φ(y x,q,p;t (0)) -φ(y x ,q,p;t (0))| , max θ∈[0,t] |γ(y x,q,p;t (θ)) -γ(y x ,q,p;t (θ))| ≤ max L φ |y x,q,p;t (0) -y x ,q,p;t (0)| , L γ max θ∈[0,t] (|y x,q,p;t (θ) -y x ,q,p;t (θ)|)

≤ L v |x -x |, where L v = max(L φ , L γ )e L f T .
Now, take h > 0 and observe that v(x, q, p, t) ≥ γ(x). Then, |v(x, q, p + h, t + h) -v(x, q, p, t)| ≤ max v(y x,q,p+h;t+h (t), q, p, t) -v(x, q, p, t) , max θ∈[t,t+h] γ(y x,q,p;t+h (θ)) -γ(x)

≤ max L v y x,q,p+h;t+h (t) -x , L γ max θ∈[t,t+h] |y x,q,p+h;t+h (θ) -x| ≤ L f L v h.
In order to proceed to the HJB equations, define the Hamiltionian to be

H(t, x, q, z) = sup u∈U (q) f (t, x, u, q) • z. (3.11)
Before stating the next result, we recall the notion of viscosity solution [START_REF] Crandall | User's guide to viscosity solutions of second order partial differential equations[END_REF] used throughout this paper for the equation:

∂ t v + ∂ p v + H(t, x, q, ∇ x v) v -γ(x) = 0 if (x, q, p, t) ∈ Ω (3.12a) v(x, q, p, t) = (M + v)(x, q, p, t) if p = 0, (3.12b)
v(x, q, p, t) = max(φ(x), γ(x)) if t = 0, (3.12c) Definition 3.5. A function u 1 (resp. u 2 ) upper semi-continuous (u.s.c.) (resp. lower semi-continuous (l.s.c)) is a viscosity subsolution (resp. supersolution) of (3.12) if for every continuously differentiable function ψ such that u 1 -ψ has a local maximum (resp. u 2 -ψ has a local minimum) at (x, q, p, t) ∈ Ω we have:

∂ t ψ + ∂ p ψ + H(t, x, q, ∇ x ψ) u 1 -γ(x) ≤ 0 if (x, q, p, t) ∈ Ω u 1 (x, q, p, t) ≤ (M + u 1 )(x, q, p, t) if p = 0, u 1 (x, q, p, t) ≤ max(φ(x), γ(x)) if t = 0,
(with the inequalities signs inversed and M -instead of M + for u 2 ). A bounded function u is a (viscosity) solution of (3.12) if u * is a subsolution and u * is a supersolution (where, we recall, u * is the upper semi-continuous envelope and u * the lower semi-continuous envelope of u).

The next statement shows that the value function defined in (2.11) is a solution of the quasi-variational system (3.12).

Theorem 3.6. Assume (H1)-(H2). Let T be a given finite horizon. Let the Lipschitz functions φ and γ be defined by (2.9) and (2.10) respectively. Then, the Lipschitz, bounded value function v defined in (2.11) is a viscosity solution of the quasi-variational inequality (3.12)

on Ω = X × Q × (0, T ) × (0, T ].
Proof. By definition, v satisfies the initial condition (3.12c). The boundary condition (3.12b) is deducted from proposition 3.3. Now, we proceed to show that (i) v is a supersolution and (ii) a subsolution of (3.12a):

First, let us prove the supersolution property (i). To satisfy min(A, B) ≥ 0 one needs to show A ≥ 0 and B ≥ 0. Since v -γ(x) ≥ 0, it is immediate that B ≥ 0. Now, consider 0 < h ≤ p ∧ t. Let ψ be a continuously differentiable function such that v -ψ attains a minimum at (x, q, p, t). Then, using proposition 3.3-(iii) and selecting an -optimal controller, dependent on h, with associated trajectory y x,q,p;t , it follows that ψ(x, q, p, t) = v(x, q, p, t) ≥ inf S x,q,p [0,t] v(y x,q,p;t (t -h), q, p -h, t -h) ≥ v(y x,q,p;t (t -h), q, p -h, t -h) -h = ψ(y x,q,p;t (t -h), q, p -h, t -h) -h and then, ψ(x, q, p, t) -ψ(y x,q,p;t (t -h), q, p -h, t -h) ≥ -h .

Since the control domain is bounded and using the continuity of f and ψ we divide by h and take the limit h → 0 to obtain

∂ t ψ + ∂ p ψ + H(t, x, q, ∇ x ψ) ≥ -
and conclude that A ≥ 0 by the arbitrariness of .

For (ii), observe that for min(A, B) ≤ 0 it suffices to show that A ≤ 0 or B ≤ 0. If v(x, q, p, t) = γ(x), it implies B ≤ 0. On the contrary, if v(x, q, p, t) > γ(x), then there exists a Σ h ≥ 0 small enough so that v(y u x,q,p;t (t -h), q, p -h, t -h) > max

θ∈[t-h,h]
γ(y u x,q,p;t (θ))

strictly, using the Lipschitz continuity of f, p and the compactness of U (which ensures the trajectories will remain near each other). Thus, proposition 3.3(iii) yields v(x, q, p, t) = inf

y u ∈S x,q,p [t-h,t]
v(y u x,q,p;t (t -h), q, p -h, t -h).

Let ψ be a continuously differentiable function such that v -ψ attains a maximum at (x, q, p, t). Fix an arbitrary u ∈ U and consider a constant control u(s) = u for t -h < s < t. Also, without loss of generality, assume that v(x, q, p, t) = ψ(x, q, p, t). Hence, v(x, q, p, t) ≤ v(y u x,q,p;t (t -h), q, p -h, t -h) ≤ ψ(y u

x,q,p;t (t -h), q, p -h, t -h)

and by dividing by h and taking h → 0 one obtains

∂ t ψ + ∂ p ψ + f (t, x, u) • ∇ x ψ ≤ 0.
Since u is arbitrary and admissible, we conclude that A ≤ 0, which completes the proof.

Theorem 3.6 provides a convenient way to characterize the value function whose level-set is the reachable set defined in (2.9). However, in order to be sure that the solution that stems from (3.12) corresponds to (2.11), a uniqueness result is necessary. This is achieved by a comparison principle which is stated in the next theorem. We recall that BU SC(Ω) and BLSC(Ω) respectively are the space of bounded u.s.c. and bounded l.s.c. functions defined over the set Ω.

Theorem 3.7. Assume (H1)-H2). Let T be a given finite horizon and denote Ω = X × Q × (0, T ) × (0, T ]. Let u 1 ∈ BU SC(Ω) and u 2 ∈ BLSC(Ω) be, respectively, sub-and supersolution of (3.12). Then,

u 1 ≤ u 2 in Ω.
The proof is inspired by earlier work on uniqueness results for hybrid control problems. The idea is to show that u 1 ≤ u 2 in all domain Ω and then on the boundary p = 0. The main difficulty arises when dealing with points in the boundary p = 0 where the system has a switching condition given by a non-local switch operator. This is tackled by the utilization of "friendly giant"-like test functions [START_REF] Barles | Unbounded viscosity solutions of hybrid control systems[END_REF], [START_REF] Ley | Lower-bound gradient estimates for first-order Hamilton-Jacobi equations and applications to the regularity of propagating fronts[END_REF]. Classically, these functions are used to prove uniqueness for elliptic problems with unbounded value functions where they serve to localize some arguments regardless of the function's possible growth at infinity. This feature proves itself very useful in our case because one can properly split the domain in no-switching and switching regions. In this work, the lag condition for the switch serves as an equivalent to the "landing condition"-which states that after an autonomous switch the system must land at some positive distance away from the autonomous switch set [START_REF] Dharmatti | Hybrid control systems and viscosity solutions[END_REF], [START_REF] Zhang | Optimal control of hybrid systems and a systems of quasi-variational inequalities[END_REF].

Proof. Let Ω be defined as above,

∂Ω| T = X × Q × (0, T ) × {0} and ∂Ω| P = X × Q × {0} × (0, T ].
First, the comparison principle is proved for ∂Ω| T (case 1), followed by Ω (case 2) and finally for ∂Ω| P (case 3), which concludes the proof for Ω.

Case 1: At a point (x, q, p, t) ∈ ∂Ω| T , from the sub-and supersolution properties,

u 1 (x, q, p, 0) -max(φ(x), γ(x)) ≤ 0, -u 2 (x, q, p, 0) + max(φ(x), γ(x)) ≤ 0, which readily yields u 1 ≤ u 2 in ∂Ω| T .
Case 2: Start by using to sub-and supersolution properties of u 1 , u 2 to obtain, in Ω,

min(∂ t u 1 + ∂ p u 1 + H(t, x, q, ∇ x u 1 ), u 1 -γ(x)) ≤ 0, (3.13) min(∂ t u 2 + ∂ p u 2 + H(t, x, q, ∇ x u 2 ), u 2 -γ(x)) ≥ 0. (3.14)
Expression (3.14) implies that both

u 2 ≥ γ(x) (3.15)
and

∂ t u 2 + ∂ p u 2 + H(t, x, q, ∇ x u 2 ) ≥ 0. (3.16)
From (3.13), one has to consider two possibilities. The first one is when u 1 ≤ γ(x).

If so, together with (3.15), one has immediately

u 1 ≤ u 2 . Now, if ∂ t u 1 + ∂ p u 1 + H(t, x, q, ∇ x u 1 ) ≤ 0, one turns to (3.16). Define v = u 1 -u 2 . Notice that v ∈ BU SC(Ω). The next step is to show that v is a subsolution of ∂ t v + ∂ p v + H(t, x, q, ∇ x v) = 0 (3.17) at (x, q, p, t).
Let ψ ∈ C 2 (Ω), bounded, be such that v -ψ has a strict local maximum at (x, q, p, t) ∈ Ω. Define auxiliary functions over Ω i × Ω i , i = 0, 1 as

Φ i (x, p, t, ξ, π, ς) = u 1 (x, i, p, t) -u 2 (ξ, i, π, ς) -ψ(x, p, t) (3.18)
- |x -ξ| 2 2 - |p -π| 2 2 - |t -ς| 2 2 .
Because the boundedness of ψ, u 1 and u 2 the suprema points are finite, for each i = 0, 1. Denote (x , p , t , ξ , π , ς ) ∈ Ω q × Ω q a point such that Φ q (x , p , t , ξ , π , ς ) = sup Ω q ×Ω q Φ q (x, p, t, ξ, π, ς).

The following lemma (proved further below for readability) establishes some estimations needed further in the proof: Lemma 3.8. Define Φ i and (x , p , t , ξ , π , ς ) as above. Then, as → 0,

|x -ξ | 2 → 0, |p -π | 2 → 0, |t -ς | 2 → 0, |x -ξ | → 0, |p -π | → 0, |t -ς | → 0,
and (x , p , t , ξ , π , ς ) → (x, p, t, x, p, t) A straightforward calculation allows to show that there exists a, b ∈ IR such that

(a, b, D ) ∈ D -u 2 (ξ , q, π , ς ) (a + ∂ t ψ, b + ∂ p ψ, D + ∇ x ψ) ∈ D + u 1 (x , q, p , t ),
where D -, D + respectively denote the sub-and super differential [START_REF] Crandall | User's guide to viscosity solutions of second order partial differential equations[END_REF] and

D = 2|x -ξ |/ , which implies a + b + H(ς , ξ , q, D ) ≥ 0 a + ∂ t ψ + b + ∂ p ψ + H(t , x , q, D + ∇ x ψ) ≤ 0,
which in turn yields, as → 0,

∂ t ψ + ∂ p ψ -L f |∇ x ψ| ≤ 0
at (x, q, p, t) ∈ Ω. By adequately choosing the test functions ψ, one can repeat the arguments to show that this assertion holds for any point in Ω. Thus, this establishes that v is a subsolution of (3.17) in Ω. Now, take κ > 0 and define a non-decreasing differentiable function χ κ : (-∞, 0) → IR + such that

χ κ (x) = 0, x ≤ -κ ; χ κ (x) → ∞, x → 0.
Take η > 0, and define a test function

ν(x, p, t) = ηt 2 + χ κ (-p).
Observe that v -ν achieves a maximum at a finite point (x 0 , q, p 0 , t 0 ) ∈ Ω. Since κ can be made arbitrarily small one can consider p 0 > κ without loss of generality. Therefore, using the subsolution property of v, by a straightforward calculation one has 2ηt 0 ≤ 0, since χ η (-p 0 ) = 0. The above inequality implies that t 0 = 0. Noticing that ν(x 0 , p 0 , t 0 ) = v(x 0 , q, p 0 , t 0 ) = 0, it follows v(x, q, p, t) ≤ ηt 2 + χ κ (-p) for all t ∈ (0, T ], x ∈ X and p > κ. Letting η → 0, κ → 0 and from the arbitrariness of q, we conclude that v ≤ 0 in Ω.

Case 3: In this case the switch lock variable arrives at the boundary of the domain, incurring thus a switch, as all others variables remain inside the domain. For all (x 0 , q 0 , p 0 , t 0 ) ∈ ∂Ω| P , for any p ≥ δ one has (using case 2 and noticing that M + u 1 = M u 1 and M -u 2 = M u 2 ) (M + u 1 )(x 0 , q 0 , p 0 , t 0 ) ≤ u 1 (x 0 , q 0 , p, t 0 ) ≤ u 2 (x 0 , q 0 , p, t 0 ).

Taking the infimum with respect to p, the above expression yields M + u 1 ≤ M -u 2 in ∂Ω| P . This suffices to conclude, since that by the sub-and supersolution properties

v = u 1 -u 2 ≤ M + u 1 -M -u 2 .
Now we present the proof of Lemma 3.8:

Proof. Writing 2Φ i (x , p , t , ξ , π , ς ) ≥ Φ i (x , p , t , x , p , t ) + Φ i (ξ , π , ς , ξ , π , ς ), for i = 0, 1, one obtains, |x -ξ | 2 + |p -π | 2 + |t -ς | 2 ≤ (u 1 + u 2 )(x , i, p , t ) -(u 1 + u 2 )(ξ , i, π , ς ) + ψ(x , p , t ) -ψ(ξ , π , ς ),
which means that, since ψ, u 1 and u 2 are bounded that

|x -ξ | 2 ≤ C * , |p -π | 2 ≤ C * , |t -ς | 2 ≤ C * , (3.19)
where C * depends on the sup

|u 1 |, sup |u 2 |, sup |ψ| and is independent of . Expression (3.19) yields |x -ξ | ≤ √ C * , |p -π | ≤ √ C * , |t -ς | ≤ √ C * .
which implies that the doubled terms tend to zero. Since (x, q, p, t) is a strict maximum of v -ψ, one gets (x , p , t , ξ , π , ς ) → (x, p, t, x, p, t). Remark that, since p > 0, one can always choose a suitable subsequence n → 0 such that all p n > 0, avoiding thus touching the switching boundary.

4. Numerical Analysis.

4.1. Numerical Scheme and Convergence. Equations (3.12) can be solved using a finite difference scheme on a domain Ω = X × Q × (0, T ) × (0, T ], for T > 0. This section proposes a class of discretization schemes and shows its convergence using the Barles-Souganidis [START_REF] Barles | Convergence of approximation schemes for fully nonlinear second order equations[END_REF] framework.

Set mesh sizes ∆x > 0, ∆p > 0, ∆t > 0 and denote the discrete grid point by (x I , p k , t n ), where x I = I∆x with I ∈ Z d , p k = k∆p with k = 0, • • • , n p , and t n = n∆t with n = 0, • • • , n t where n p = T /∆p and n t = T /∆t. The approximation of the value function is denoted by

v(x I , q, p k , t n ) = v n Ik (q)
and the penalization functions are denoted by φ(x I ) = φ I , γ(x I ) = γ I . Define the following grids:

G # = ∆xZ d × Q × ∆p{0, 1, • • • , n p } × ∆t{0, 1, • • • , n t }, G # p = ∆t{0, 1, • • • , n t } × ∆xZ d × Q,
and the discrete space gradient at point x I for any general function µ:

D ± µ(X I ) = D ± µ I = D ± x1 µ I , • • • , D ± x d µ I ,
where

D ± xj µ I = ± µ I j,± -µ I ∆x , with I j,± = (i 1 , • • • , i j-1 , i j ± 1, i j+1 , • • • , i d ).
Define a numerical Hamiltonian H : G # p × IR d × IR d → IR destined to be an approximation of H. We assume that H verifies the following hypothesis: (H4) There exists L H1 , L H2 > 0 such that, for all t, x, q ∈ G # p and A

+ , A -, B + , B -∈ IR d , |H(t, x, q, A + , A -) -H(t, x, q, B + , B -)| ≤ L H1 (||A + -B + || + ||A --B -||) ||H(t, x, q, A + , A -)|| ≤ L H2 (||A + + A -||).
(H5) The Hamiltonian satisfies the monotonicity condition for all t, x, q ∈ G # p and almost every A + , A -∈ IR d :

∂ A + i H(t,
x, q, A + , A -) ≤ 0, and ∂ A - i H(t, x, q, A + , A -) ≥ 0.

(H6) There exists L H3 > 0 such that for all t, x, q ∈ G # p , t , x , q

∈ [0, ∆tn t ] × X × Q and A ∈ IR d , |H(t, x, q, A, A) -H(t , x , q , A)| ≤ L H3 (|t -t | + ||x -x || + |q -q |).
Let Φ : Ω → IR, h = (∆x, ∆p, ∆t) and set S Ω h (x, q, p, t, λ; Φ) = min λ -γ(x), H(t, x, q, D + Φ(x, q, p, t -∆t), D -Φ(x, q, p, t -∆t))+ λ -Φ(x, q, p, t -∆t) ∆t + λ -Φ(x, q, p -∆p, t -∆t) ∆p .

Now, consider the following operators:

S h (x, q, p, t, λ; Φ) := S Ω h (x, q, p, t, λ; Φ) if (x, q, p, t) ∈ Ω λ -min w∈W (x,q),p ≥δ Φ(x, g(w, q), p , t) if p = 0, (4.1) and

F(x, q, p, t, u, ∇u) := u -γ(x) ∂ t u + ∂ p u + H(t, x, q, ∇ x u) if (x, q, p, t) ∈ Ω u(x, q, p, t) -(M u)(x, q, p, t) if p = 0. (4.
2) With these notations, the equation (3.12) is equivalent to F(x, q, p, t, v, ∇v) = 0 in Ω.

We define an approximation scheme by: S h (x, q, p, t, u h (x, q, p, t), u h ) = 0 in G # .

∂ A + i H + ∂ A - i H ≤ 1 (4.4)
the discretization scheme (4.3) is stable, monotone and consistent. Moreover, u h converges towards the function v, as h → 0.

Proof. The proof follows the lines used in the framework of Barles-Souganidis [START_REF] Barles | Convergence of approximation schemes for fully nonlinear second order equations[END_REF] inside Ω. We then complete the proof with the case p = 0. The goal is to show that the numerical scheme solutions' envelopes u(x , q , p , t ) = lim inf (x,q,p,t)→(x ,q ,p ,t ) h→0 u h (x, q, p, t) u(x , q , p , t ) = lim sup (x,q,p,t)→(x ,q ,p ,t ) h→0 u h (x, q, p, t), are respectively supersolution and subsolution of (4.2). Then, using the comparison principle in theorem 3.7, one obtains u ≤ u. However, since the inverse inequality is immediate (using the definition of limsup and liminf, one gets u ≡ u = u achieving thus the convergence.

Only the subsolution property of u is presented next, the proof of the supersolution property of u being very alike.

Inside Ω it is sufficient to show that S Ω h is stable, monotone and consistent. Observe that S h is proportional to -Φ in the terms outside the Hamiltonian. Since fluxes H are monotone by hypothesis (H7) (see [START_REF] Osher | Higher order essentially non-oscillatory schemes for hamilton-jacobi equations[END_REF] for details in monotone Hamiltonian fluxes) whenever the CFL condition (4.4) is satisfied, the monotonicity of S h follows. The stability is ensured by the boundedness of Φ and hypothesis (H6). Finally, hypothesis (H8) and lemma 3.2 are used in a straightforward fashion to obtain the consistency properties below: lim sup (x ,q ,p ,t )→(x,q,p,t) h→0

S h (x , q , p , t , λ; Φ) ≤ F * (x, q, p, t, Φ, ∇Φ) lim inf (x ,q ,p ,t )→(x,q,p,t) h→0 S h (x , q , p , t , λ; Φ) ≥ F * (x, q, p, t, Φ, ∇Φ)

Now, choose Φ ∈ C ∞ b (Ω) such that u-Φ has a strict local maximum at (x 0 , q 0 , p 0 , t 0 ) ∈ Ω (without loss of generality assume (u -Φ)(x 0 , q 0 , p 0 , t 0 ) = 0). First, suppose p 0 > 0. Then there exists a ball centered in (x 0 , q 0 , p 0 , t 0 ) of radius r > 0 such that u(x, q, p, t) ≤ Φ(x, q, p, t), ∀(x, q, p, t) ∈ B((x 0 , q 0 , p 0 , t 0 ), r) ⊂ Ω. Construct sequences (x , q , p , t ) → (x 0 , q 0 , p 0 , t 0 ) and h → 0 as → 0 such that u h (x , q , p , t ) → u(x 0 , q 0 , p 0 , t 0 ) and (x , q , p , t ) is a maximum of u h -Φ in B((x 0 , q 0 , p 0 , t 0 ), r). Denote ζ = (u h -Φ)(x , q , p , t ). (Remark that ζ → 0 as → 0).

Then, u h ≤ Φ+ζ inside the ball and since S h (x , q , p , t , u h (x , q , p , t ); u h ) = 0, by the monotonicity property one obtains S h (x , q , p , t , Φ(x , q , p , t ) + ζ ; Φ + ζ ) ≤ 0.

Taking the limit (inf) → 0 together with the consistency of the scheme, one obtains the desired inequality F * (x 0 , q 0 , p 0 , t 0 , Φ, ∇Φ) ≤ 0. Suppose now that p 0 = 0. Construct sequences (x , q , p 0 , t ) → (x 0 , q 0 , p 0 , t 0 ) and h → 0 as → 0 such that u h (x , q , p 0 , t ) → u(x 0 , q 0 , p 0 , t 0 ). Then, lim inf

x →x0 q →q0 t →t0 h →0 S h (x , q , p 0 , t , Φ(x , q , p 0 , t ), Φ) = lim inf x →x0 q →q0 t →t0 h →0 (Φ -M Φ)(x , q , p 0 , t ) = (Φ -(M Φ) * )(x 0 , q 0 , p 0 , t 0 ).
Since each u h is a solution of (4.3), using lemma 3.2 the above expression yields at the point (x 0 , q 0 , p 0 , t 0 ):

0 = (Φ -(M Φ) * )(x 0 , q 0 , p 0 , t 0 ) ≥ (Φ -(M Φ) * )(x 0 , q 0 , p 0 , t 0 ) ≥ (Φ -(M + Φ))(x 0 , q 0 , p 0 , t 0 ) = F * (x 0 , q 0 , p 0 , t 0 , Φ, ∇Φ)
achieving the desired inequality.

Numerical Simulations.

In this section, we give a simple example to illustrate the convergence result. For simplicity, we take here d = 2 and consider the numerical Hamiltonian H given by the monotone Local Lax-Friedrichs scheme [START_REF] Osher | Higher order essentially non-oscillatory schemes for hamilton-jacobi equations[END_REF] (where the two components of the gradient are explicit):

H t, x, q; a + , a -, b + , b -= H t, x, q; a + + a - 2 , b + + b - 2 - c a a + -a - 2 -c b b + -b - 2
where a ± = D ± i v, b ± = D ± j v and the constants c a , c b are defined as c a = sup t,x,q,(r1,r2)

|∂ r1 H(t, x, q; r 1 , r 2 )| (4.5)

c b = sup t,x,q,(r1,r2) |∂ r2 H(t, x, q; r 1 , r 2 )|. ( 4.6)
This particular discretization satisfies hypothesis (H4)-(H6) and therefore is in the setting of Proposition 4.1.

Taking v n the approximate solution of v at t n , the equation

S h (x I , q, p k , t n+1 , v n+1 Ik (q); v n ) = 0
allows an explicit expression of v n+1 () as a function of past values v n ():

v n+1 Ik (q) =      γ I v n Ik (q) -∆t v n Ik (q)-v n Ik-1 (q) ∆p + H(t n , x I , q, D -v n Ik (q), D + v n Ik (q)) if (I, q, k, n) ∈ G # min w∈W (x I ,q),k ≥δ/∆p v n Ik (g(w, q)) if k = 0. (4.7)
In order to illustrate the convergence of this particular numerical scheme, we present simulations using a simple hybrid vehicle model (see Section 2.1). Here the vehicle's energy state is a two-dimensional vector y ∈ X = IR 2 , where y = (y 1 , y 2 ) denotes the state of charge of the battery and the fuel available in the fuel tank. These two quantities are the image of the remaining energy and have to be constrained to stay in the compact set K = [0, 1] 2 , where the energies quantities are normalized. The discrete variable q ∈ Q = {0, 1} is the ICE state, indicating whether the ICE is off (q = 0) or on (q = 1). The power output is a measurable function u(•) ∈ U where U is the admissible control set that is taken here as U = [0, u max ]. We consider that the switch dynamics is given by g(w, q) = |q -w|. Test 1. The energetic dynamical model is given by f (t, x, u, q) = (-a 1 + qu, -q(a 2 + u)), where a 1 , a 2 > 0 are constant depletion rates of the battery's electric energy and the reservoir's fuel (whenever the ICE is on), respectively. The simulated instances use a 1 = 0.10, a 2 = 0.15 and u max = 0.07.

We test an instance where the set of initial positions C is the square centered at (x 1 , x 2 ) = (0.5, 0.5) and with half-lenght equal to 0.05. The computations are performed 1 for (y 1 , y

2 ) ∈ [-0.1, 1.1] 2 , Q = 0, 1, p ∈ [0, T ],
where the final horizon is 1 on 64-bit Mac OS using a 1.8GHz Intel Core i7 processor, with 4Gb of RAM chosen as T = 5.6.

Remark 4.1. Considering the above dynamics, an exact autonomy of the system can be evaluated analytically. By autonomy, we mean the longest time during which the state remains inside K. Equivalently, the autonomy is the first time when the reachable set is empty: T * = inf{s | R C (s) = ∅}. Indeed, one can readily see that if no more admissible energy states are attainable after T * , any admissible trajectory must come to a stop beyond this time. For this toy problem, given an initial conditions (x 1 , x 2 ), the shortest time to empty the fuel reservoir is given by t * = x 1 /(a 2 + u max ). The charge left in the battery evaluated at this instant is given by x 1 (t * ) = x 1 (0)t * (a 1 -u max ). If x 1 (t * ) ≤ 0, it means the fuel cannot be consumed fast enough before the battery is depleted. This condition can be expressed in terms of the parameters of the model as x 1 (a 2 + u max ) ≤ x 2 (a 1 -u max ). In this case, the autonomy is given by T * = x 1 /(a 1 -u max ). Otherwise, the autonomy is given by T * = (x 1 + u max t * )/a 1 .

Simulations are running by fixing the CFL number to 0.9 in the condition (4.4) and then using several discretization steps ∆x 1 = ∆x 2 = ∆x and ∆p. Since the exact value function is not known, the convergence error is analyzed with respect to a reference solution V # computed with ∆x = 0.01, ∆p = 0.028. The L ∞ error, as a function of the discretization step sizes ∆x, ∆p, is then computed as being e(∆x, ∆p) = sup (x,p,s,q)∈G # (∆x,∆p)

| v # (∆x, ∆p) -V # |, (4.8)
where G # (∆x, ∆p) is the numerical grid obtained using discretization steps (∆x, ∆p) and v # (∆x, ∆p) the corresponding numerical solution. Table 4.1 presents the L ∞error and the total CPU running times as a function of ∆x, ∆p. In these simulations, δ is equal to 1. As can be seen from the numerical table, the error goes to 0 when both ∆x and ∆p tend to 0.

Test 2

The previous example aimed to show the convergence of the numerical method on a very simple model. Here we consider another model where the optimal trajectories may have more than one switch. The reachable set depends on the value of the lag variable δ. Set f (t, x, u, q) = (-0.3, -0.3(2q -1)), and consider C the square centred at (0.8, 0.9) T and with half lenght 0.04. The set of constraints K is [-0.4, 1.1] 2 . [-0.3, 1.2] 2 × [0, T ], with T = 1.5, ∆x 1 = ∆x 2 = 0.009, ∆p = 0.015, and the CFL number is fixed to 0.6. In this example, the optimal strategies include several switches in order to keep the trajectories inside the set K. However, the number of possible switches depends on the value of δ. When δ is very small, the number of possible switches is large enough and the trajectories evolve in a large reachable set. If δ is large, then the number of possible switches decreases and the reachable set becomes smaller. Despite these illustrations of a convergent numerical scheme, we believe that a finer error analysis deserves further attention. This paper proofs the convergence of the numerical scheme, which is a first step in the numerical analysis of the SQVI. A more complete study should investigate more in detail the rate of convergence of numerical schemes, and the sensitivity analysis with respect to δ.

(3. 8 )

 8 Proof. The dynamic programming principle is composed of three parts. (i): Equality (3.6) is obtained directly by definition (2.11).

(4. 3 ) 4 . 1 .

 341 Proposition Let Φ ∈ C ∞ b (Ω).Under hypothesis (H1)-(H2), (H4)-(H6) and the CFL condition

Figure 4 .

 4 Figure 4.1 shows, for different values of ∆x and ∆p, the minimum time values when the value function first crosses the 0-level set, i.e. T (x) = min{t | ∃q, p, v(x, q, p, t) ≤ 0}. The function T represents for each x the first time the position is reached by an admissible trajectory starting from C.

Figure 4 .

 4 [START_REF] Bardi | Optimal control and viscosity solutions of Hamilton-Jacobi-Bellman equations[END_REF] shows the minimum time functions corresponding to different values of the lag δ. The simulations are performed on the computational domain

  (a) ∆x = 0.05, ∆p = 0.112. (b) ∆x = 0.02, ∆p = 0.56. (c) ∆x = 0.01, ∆p = 0.028.

Figure 4 . 1 .

 41 Figure 4.1. Test 1. Contour plots of the minimum time function: Three discretization steps in ∆x and ∆p.

  (a) δ = 0.02. (b) δ = 0.4. (c) δx = 1.

Figure 4 . 2 .

 42 Figure 4.2. Test 2. Contour plots of the minimum time functions corresponding to three different values of δ.

Table 4 . 1

 41 Convergence results and running times of an instance using δ = 1.

		∆p = 0.0112	∆ p = 0.056	∆ p = 0.028
	∆x	e(∆x, ∆p) CPU (s) e(∆x, ∆p) CPU (s) e(∆x, ∆p) CPU (s)
	0.05	8.10e-2	7.8	7.23e-2	16.7	7.16e-2	50.1
	0.04	5.462-2	10.8	4.61e-2	26.4	4.49e-2	77.7
	0.03	3.17e-2	19.0	2.12e-2	47.1	1.74e-2	170.7
	0.02	2.91e-2	45.0	1.50e-2	114.1	1.27e-2	451.2

* This work is co-funded by Renault SAS under grant ANRT CIFRE n 928/2009. The second author is partially supported by the EU under the 7th Framework Programme Marie Curie Initial Training Network "FP7-PEOPLE-2010-ITN", SADCO project, GA number 264735-SADCO. The authors are deeply grateful for the corrections and the feedback of the reviewers of this article.