acmcopyright \isbn978-1-4503-3955-1/16/04\acmPrice$15.00 \acmPrice$15.00
Robust Asymptotic Stabilization of Hybrid Systems
using Control Lyapunov Functions
Abstract
We propose tools for the study of robust stabilizability and the design of robustly stabilizing feedback laws for a wide class of hybrid systems given in terms of hybrid inclusions with inputs and disturbances. We introduce notions of robust uniform global stabilizability and stabilization that capture the case when disturbances can be fully rejected, practically rejected, and when they induce a residual set that can be stabilized. Robust control Lyapunov functions are employed to determine when stabilizing static state-feedback laws are available and also to synthesize robustly stabilizing feedback laws with minimum pointwise norm. Sufficient conditions on the data of the hybrid system as well as on the control Lyapunov function are proposed for the said properties to hold. An example illustrates the results throughout the paper.
doi:
http://dx.doi.org/10.1145/2883817.2883848keywords:
Hybrid systems; Robust stability; Control Lyapunov functions¡ccs2012¿ ¡concept¿ ¡concept_id¿10003752.10003753.10003765¡/concept_id¿ ¡concept_desc¿Theory of computation Timed and hybrid models¡/concept_desc¿ ¡concept_significance¿500¡/concept_significance¿ ¡/concept¿ ¡concept¿ ¡concept_id¿10010147.10010178.10010213¡/concept_id¿ ¡concept_desc¿Computing methodologies Control methods¡/concept_desc¿ ¡concept_significance¿500¡/concept_significance¿ ¡/concept¿ ¡concept¿ ¡concept_id¿10010583.10010750.10010758.10010759¡/concept_id¿ ¡concept_desc¿Hardware Process variations¡/concept_desc¿ ¡concept_significance¿300¡/concept_significance¿ ¡/concept¿ ¡/ccs2012¿
[500]Theory of computation Timed and hybrid models \ccsdesc[500]Computing methodologies Control methods \ccsdesc[300]Hardware Process variations \printccsdesc
1 Introduction
Recent advances in the theory of hybrid dynamical systems have provided powerful tools for the study of robustness of asymptotic stability. One of the main results in [Goebel.ea.11], which is for hybrid systems modeled as hybrid inclusions, is that asymptotic stability of a compact set is nominally robust when the objects defining the hybrid system satisfy mild regularity properties – by nominal robustness we mean that the stability property is be preserved semiglobally and practically for small enough perturbations. The importance of this result for control design is significant, as it highlights structural properties that the interconnection between the plant and the controller (both potentially hybrid) should satisfy so that, after a perturbation-free design, the behavior of the closed-loop system does not change much when small perturbations are present (even when those perturbations may affect the times at which flows and jumps occur). The case of large disturbances in hybrid systems was studied in [Cai.Teel.09] using the notion of input-to-state stability (ISS). While the results therein involving ISS Lyapunov functions can certainly be used for design, constructive design tools that guarantee robustness of asymptotic stability to large disturbances are not yet available.
Control Lyapunov functions have been shown to be very useful in constructively designing feedback control algorithms [SontagSYSCON89, Clarke00, Sontag.Sussman.96, FreemanKokotovic96]. In particular, in [FreemanKokotovic96], tools for the design of robustly stabilizing feedback controllers are proposed for continuous-time systems for which a robust control Lyapunov function exists. A salient feature of using robust control Lyapunov functions is that, even under the presence of large disturbances, an asymptotic stability of a set, typically defined by a residual neighborhood around the desired equilibrium, can be guaranteed. Recently, the concept of control Lyapunov function was extended to different classes of hybrid systems without disturbances, see [Sanfelice.11.TAC.CLF] for results for hybrid inclusions and [DiCairano.ea.14.TAC] for results for discrete-time systems with continuous and discrete states.
Motivated by the constructive design tools for robust stability in [FreemanKokotovic96], in this paper, we propose tools for the study of robust stabilizability and the design of robustly stabilizing feedback laws that employ control Lyapunov functions for hybrid systems with disturbances. For a wide class of hybrid systems given in terms of hybrid inclusions with inputs and disturbances, we introduce notions of robust uniform global stabilizability and stabilization that capture the case when disturbances can be fully rejected, practically rejected, and when they induce a residual set that can be stabilized. Building from results in [Sanfelice.11.TAC.CLF], we propose conditions guaranteeing the existence of a continuous robust stabilizing static state-feedback law. We show that, under further conditions, continuous state-feedback laws with minimum pointwise norm can be constructed.
The remainder of this paper is organized as follows. In Section 2, we introduce the hybrid system model and related notions. The notions of robust stability, stabilizability, and control Lyapunov functions are introduced in Section 3. Conditions guaranteeing the existence of stabilizing feedback laws are given in Section 4, while the constructive design tools are in Section 5. Due to space constraints, the proof of the results are not included but will be published elsewhere.
Notation: denotes -dimensional Euclidean space. denotes the real numbers. denotes the nonnegative real numbers, i.e., . denotes the natural numbers including , i.e., . denotes the closed unit ball in a Euclidean space. Given a set , denotes its closure. Given a set , denotes its boundary. Given , denotes the Euclidean vector norm. Given a closed set and , . Given vectors and , denotes their inner product and, at times, we write simply as . A function is positive definite with respect to a set if for each and for each . A function is said to belong to class- if it is continuous, zero at zero, and strictly increasing. A function is said to belong to class- if it is an unbounded class- function. A function is a class- function, also written , if it is nondecreasing in its first argument, nonincreasing in its second argument, for each , and for each . Given a locally Lipschitz function , denotes the Clarke generalized derivative of at in the direction of ; see [Clarke90]. Given a map , its graph is denoted by . Given a set , and .
2 Hybrid Systems with Inputs and Disturbances
A hybrid system with state , control input , and disturbance input is given by
(3) |
The space for the state is , the space for the input is , where and , and the space for the disturbance is , where and . The data defining is as follows:
-
•
The set is the flow set;
-
•
The set-valued map is the flow map;
-
•
The set is the jump set;
-
•
The set-valued map is the jump map.
The sets and in the definition of define conditions that , , and should satisfy for flows or jumps to occur. Throughout this paper, we assume that these sets impose conditions on that only depend on and conditions on that only depend on .
The state of the hybrid system can include multiple logic variables, timers, memory states as well as physical (continuous) states, e.g., is a state vector with a state component given by a logic variable taking values from a discrete set , a state component given by a timer taking values from the interval , where is the maximum allowed value for the timer, and with a state component representing the continuously varying state – note that in such a case, can be embedded in for .
Given a set with being either or , , , , and , we define
-
•
-
•
The projector onto the state space
-
•
The projector onto the state and input space
-
•
The projector onto the input and disturbance space
and
for each , respectively;
-
•
The projector onto the flow input, flow disturbance, jump input, and jump disturbance space
for each , respectively.
That is, given a set , denotes the “projection” of onto , denotes the “projection” of onto , while, given , denotes the set of values such that ; similarly for .
Solutions to hybrid systems are given in terms of hybrid arcs, hybrid disturbances, and hybrid inputs on hybrid time domains. A set is a compact hybrid time domain if
for some finite sequence of times . It is a hybrid time domain if for all ,
is a compact hybrid time domain.111This property is to hold at each , but can be unbounded. A hybrid arc is a function on a hybrid time domain that, for each , is absolutely continuous on the interval
where denotes the hybrid time domain of .
Hybrid disturbances are functions of hybrid time that will be generated by some hybrid exosystem of the form
(6) |
with state (and output) . A disturbance generated by a hybrid exosystem of the form (6) that, for given state trajectory and input, satisfies the dynamics of the hybrid system is said to be admissible. For instance, the hybrid exosystem with data
where is a constant, generates disturbances that remain in and that are Lipschitz continuous during flows (with Lipschitz constant ), but not necessarily differentiable; see [Robles.Sanfelice.11.HSCC] for constructions of hybrid exosystems generating square and triangular signals.
Similarly, control inputs are functions of hybrid time, i.e., with being a hybrid time domain, with the property that, for each , is Lebesgue measurable and locally essentially bounded on the interval . A control input satisfying these properties and, for given state trajectory and disturbance, satisfies the dynamics of the hybrid system is said to be admissible.
A solution to the hybrid system in (3) is given by , , , with and satisfying the dynamics of , where is a hybrid arc, is a hybrid input, and is a hybrid disturbance. A solution to is said to be complete if is unbounded, and is said to be maximal if there does not exist another pair such that is a truncation of to some proper subset of . For more details about solutions to hybrid systems with inputs, see [Sanfelice.11.TAC.CLF].
Next, we illustrate the modeling framework in a system that will be revisited throughout the paper. Being of second order, with jumps in both of its state variables, and exhibiting Zeno behavior for specific choices of its inputs, the system is rich enough, yet not overly complex, for the purposes of illustrating our ideas and results.
Example 2.1.
(controlled pendulum with impacts) Consider a point-mass pendulum impacting on a controlled slanted surface. Denote the pendulum’s angle (with respect to the vertical) by , where corresponds to a displacement to the right of the vertical and to a displacement to the left of the vertical. The pendulum’s velocity (positive when the pendulum rotates in the counterclockwise direction) is denoted by . When with denoting the angle of the surface, its continuous evolution is given by
where , capture the system constants (e.g., gravity, mass, length, and friction) and corresponds to torque actuation at the pendulum’s end. For simplicity, we assume that and . The disturbance represents actuator noise and unmodeled dynamics, while represents uncertainty in the damping constant . Impacts between the pendulum and the surface occur when
(7) |
At such events, the jump map takes the form
where the functions
and
, are linear in and capture the effect of pendulum compression and restitution at impacts, respectively, as a function of . For simplicity, the function is used to capture (much more complex) rapid displacements of the pendulum at collisions by guaranteeing that at jumps – in this way, after impacts away from , the pendulum is pushed away from the contact condition. The restitution coefficient function models the effect of gravity on energy dissipation at impacts via the angle : when the surface is placed as far to the left as possible (), is given by the minimum value , while when the surface is at , takes the maximum value . The disturbance represents uncertainty in the restitution coefficient.
The model above can be captured by the hybrid system given by
(17) |
where , , with , ,
Note that the definitions of and impose state constraints on the inputs that only depend on the state .
The following mild conditions on the data of will be imposed in some of our results.
Definition 2.2.
(hybrid basic conditions) A hybrid system is said to satisfy the hybrid basic conditions if its data satisfies
-
(A1)
and are closed subsets of and , respectively;
-
(A2)
is outer semicontinuous relative to and locally bounded222A set-valued map is outer semicontinuous at if for each sequence converging to a point and each sequence converging to a point , it holds that ; see [RockafellarWets98, Definition 5.4]. Given a set , it is outer semicontinuous relative to if the set-valued mapping from to defined by for and for is outer semicontinuous at each . It is locally bounded if for each compact set there exists a compact set such that . , and for all , is nonempty and convex;
-
(A3)
is outer semicontinuous relative to and locally bounded, and for all , is nonempty.
When is single valued, (A2) reduces to being continuous. Similarly, when is single valued, (A3) reduces to being continuous.
In the sections to follow, we will design state-feedback laws to control the hybrid system . The resulting closed-loop system under the effect of the control pair is given by
(20) |
with
and
Note that when the components of and correspond to the same physical input, like in Example 2.1, such components of the feedback law pair have to be identical – see the revisited version of Example 2.1 in Example 5.4.
Remark 2.3.
When satisfies the hybrid basic conditions and the state-feedback pair is continuous, the hybrid closed-loop system satisfies the hybrid basic conditions. An important consequence of satisfying the hybrid basic conditions is that asymptotic stability of a compact set for (with ) is automatically nominally robust, in the sense that the asymptotic stability property is preserved (semiglobally and practically) under the presence of small enough perturbations.
3 Robust Stability, Stabilizability, and Control Lyapunov Functions
This section introduces the stability, stabilizability, and control Lyapunov function notions for employed throughout the paper. Nominal versions of these notions can be found in [Goebel.ea.11] and [Sanfelice.11.TAC.CLF].
First, we introduce a stability property of closed sets capturing robustness with respect to all admissible disturbances . For simplicity, we write the global version, but, though more involved, a local version can certainly be formulated.
Definition 3.1.
(-robust uniform global asymptotic stability) Given a control , and closed sets and subsets of , the set is said to be -robustly uniformly globally asymptotically stable relative to for the hybrid system if
(21) |
and there exists such that, for each admissible disturbance , every solution to using the given control satisfies
(22) |
Remark 3.2.
When the property in Definition 3.1 holds for , in which case we will drop “relative to ,” the notion resembles [Goebel.ea.11, Definition 3.6] with the addition that the property holds for every possible admissible disturbance. When , the set is a residual set relative to , meaning that complete solutions would converge to but may not converge to . A particular such situation is when is the origin and the set is a small neighborhood around it. Finally, note that the property in Definition 3.1, and the ones introduced below, may hold for a large enough residual (e.g., ), though one is typically interested in having to be some small neighborhood of .
Remark 3.3.
The property in Definition 3.1 differs from input-to-state stability (ISS) with respect to as the bound defining ISS involves the distance from the state trajectory to a set (like ), rather than to a residual set (like ), and includes an additive offset that is a function of a norm of ; see [Cai.Teel.09] for a definition of ISS for hybrid systems as in (3). A key difference is that ISS guarantees attractivity of a neighborhood of a set (of size depending on a norm of the disturbance), while our -robust notion guarantees an asymptotic stability of a residual set that is uniform over all admissible disturbances.
The existence of some control , perhaps (hybrid) time dependent, stabilizing a point or a set is known as stabilizability. Next, we introduce this notion for the case of hybrid systems under disturbances.
Definition 3.4.
(robust stabilizability) Given a hybrid system , a closed set is said to be
-
1)
-robustly uniformly globally asymptotically stabilizable for if there exists an admissible control such that the set is -robustly uniformly globally asymptotically stable for ;
-
2)
-robustly practically uniformly globally asymptotically stabilizable for if for every there exist an admissible control and a closed set satisfying
such that the set is -robustly uniformly globally asymptotically stable for relative to ;
-
3)
-robustly uniformly globally asymptotically stabilizable with residual for with closed, , if there exists an admissible control such that the set is -robustly uniformly globally asymptotically stable relative to for .
Remark 3.5.
The notion in item 1) in Definition 3.4 captures the situation when the effect of the disturbances can be overcome and the desired set rendered asymptotically stable by some control . For the hybrid system in Example 2.1, for which the desired set is naturally the origin, this set being -robustly uniformly globally asymptotically stabilizable requires the existence of a control that renders the origin uniformly globally asymptotically stable for any disturbance ; see Example 5.4. The practical notion in item 2) corresponds to the situation when the asymptotically stable residual set can be made arbitrarily close to the set by some control . Finally, item 3) captures the situation when only a residual set can be stabilized.
Methods for synthesis of feedback control laws that induce the properties introduced above will employ control Lyapunov functions. For the nominal case, a control Lyapunov function for a hybrid system is a function that, for each value of the state, there exist control input values that make the function decrease during flows and jumps [Sanfelice.11.TAC.CLF, Definition 2.1]. Following the construction in [FreemanKokotovic96, Definition 3.8] for continuous-time systems, we introduce the following robust control Lyapunov function notion for .
Definition 3.6.
(robust control Lyapunov function) Given a closed set , sets and , and sets and , a continuous function that is locally Lipschitz on an open set containing is a robust control Lyapunov function (RCLF) with controls and for if there exist333When has purely continuous dynamics, i.e., it does not exhibit jumps, then can be replaced by . In fact, in such a case, when solutions cannot flow out of . However, when the system has jumps, if (3.6) only holds for each , there could still be solutions that jump outside of . , and a positive definite function such that Remove ?
(23) | |||
(24) | |||
(25) |
Example 3.7.
(controlled pendulum with impacts (revisited)) For the hybrid system in Example 2.1, let and consider the candidate robust control Lyapunov function with controls for given by
(26) |
Condition (23) holds trivially. During flows, we have that
for all . It follows that (3.6) is satisfied with defined as for all . In fact, note that, for each ,
and that . Then
for all such that , while when , we have
For each , we have
and that . Then, at jumps, we have
for all , where
which, by the properties of and , is positive. Then, condition (3.6) is satisfied with defined as for all .
4 Robust Stabilizability via Static State-Feedback Laws
In this section, we provide conditions guaranteeing the existence of a robustly stabilizing control inducing some of the properties introduced in Section 3. Our interest is in control laws that are of (static) state-feedback type and continuous, which, as argued in Remark 2.3, when satisfies the hybrid basic conditions, would lead to a closed-loop system (without ) as in (20) satisfying the hybrid basic conditions.
Given the compact set and a robust control Lyapunov function satisfying Definition 3.6 with positive definite function and , define, for each and , the function
and, for each and , the function
When these functions and the system satisfy further properties introduced below, the existence of a -robustly stabilizing feedback law is guaranteed.
Theorem 4.1.
Given a compact set and a hybrid system satisfying the hybrid basic conditions, suppose there exists a robust control Lyapunov function with controls for that is continuously differentiable on a neighborhood of , where comes from Definition 3.6. Furthermore, suppose the following conditions hold:
-
R1)
The set-valued maps and are lower semicontinuous444A set-valued map is lower semicontinuous if for each one has that , where is the inner limit of (see [RockafellarWets98, Chapter 5.B]). By lower semicontinuity of a set-valued map with not open we mean that the trivial extension of proposed in [Sanfelice.11.TAC.CLF, Lemma 4.2] is lower semicontinuous. with convex values.
-
R2)
For every and for every , the function is convex on and, for every and every , the function is convex on .
-
R3)
The set is closed and the set-valued maps and are outer semicontinuous, locally bounded, and nonempty for each and each , respectively.
Then, for each , the set is -robustly uniformly globally asymptotically stabilizable with residual
(29) |
for by a state-feedback law that is continuous on , where is the restriction of to given by
In particular, for each , there exists a state-feedback law with continuous on and continuous on defining an admissible control that renders the compact set in (29) -robustly uniformly globally asymptotically stable relative to for .
Example 4.2.
(controlled pendulum with impacts (revisited)) A robust control Lyapunov function satisfying the conditions in Theorem 4.1 was constructed in Example 3.7. Conditions R1) and R3) immediately hold from the constructions therein. The definition of above gives, for each ,
while the definition of above gives, for each ,
Then, R2) holds. Hence, since , according to Theorem 4.1, the hybrid system in Example 2.1 has its origin -robustly practically uniformly globally asymptotically stabilizable. We will see in Example 5.4 that a non-practical property already holds and that a stabilizing state-feedback law can actually be synthesized.
The result above guarantees a robust stabilizability property that either has a residual or is practical. For robust stabilizability of a compact set, extra conditions are required to hold nearby the compact set. For continuous-time systems, such conditions correspond to the so-called small control property [SontagSYSCON89, FreemanKokotovic96, Krstic.Deng.98]. To that end, given a compact set and a robust control Lyapunov function as in Definition 3.6, define, for each , the set-valued map555Note that if either or do not intersect the compact set , then neither the existence of the functions or , respectively, nor lower semicontinuity at are needed, since R4) and R5) would hold for free.
(39) |
where and induce forward invariance of for , that is,
-
R4)
Every maximal solution to
from is such that the component satisfies for all .
-
R5)
Every maximal solution to
from is such that the component satisfies for all .
Under the conditions in Theorem 4.1, with , the maps in (39) are lower semicontinuous for every . To be able to make continuous selections at , these maps are further required to be lower semicontinuous for . These conditions resemble those already reported in [FreemanKokotovic96] for continuous-time systems.
Theorem 4.3.
Under the conditions of Theorem 4.1 and when , if there exist continuous functions and such that conditions R4) and R5) hold, and
-
R6)
The set-valued map is lower semicontinuous at each ;
-
R7)
The set-valued map is lower semicontinuous at each ;
-
R8)
The hybrid exosystem in (6) satisfies the hybrid basic conditions;
then is -robustly uniformly globally asymptotically stabilizable for by a continuous state-feedback pair .
5 Constructive Design of Robustly Stabilizing Feedback Laws
We show that, under further conditions, the results in Section 4 lead to a constructive design procedure of state-feedback control laws that induce -robust asymptotic stability. The key idea is to define a selection from the “regulation map” that can be synthesized (or computed) for given system data and RCLF.
Recalling the construction of and in Section 4, we evaluate these functions at points and with to define the functions
(40) |
and the set-valued maps
(43) |
Furthermore, define
(44) |
and
(45) |
When, for each , the functions and are convex, and the set-valued maps and have nonempty closed convex values on and , respectively, we have that and have nonempty convex closed values on (44) and on (45), respectively; this property follows from [FreemanKokotovic96SIAM, Proposition 4.4]. Then, and have unique elements of minimum norm on and , respectively, and their minimal selections
are given by
(46) | |||
(47) |
Moreover, as the following result states, these selections are continuous under further properties of and .
Theorem 5.1.
Given a compact set and a hybrid system satisfying the hybrid basic conditions, suppose there exists a robust control Lyapunov function with controls for that is continuously differentiable on a neighborhood of , where comes from Definition 3.6. Furthermore, suppose conditions R1)-R3) in Theorem 4.1 hold. Then, for each , the state-feedback law pair
defined as
renders the compact set
-robustly uniformly globally asymptotically stable for relative to , where is the restriction of to given as in Theorem 4.1. Furthemore, if the set-valued maps and have closed graph then and are continuous.
Remark 5.2.
The state-feedback law (5.1)-(5.1) asymptotically stabilizes for , but not necessarily for , as without an appropriate extension of these laws to and , respectively, there could exist solutions to the closed-loop system that jump out of . This point motivates the (non-practical, and stronger) result that we present next.
Following the ideas behind Theorem 4.3, we extend the pointwise minimum norm state-feedback control law in Theorem 5.1 so as to -robustly globally asymptotically stabilize a compact set . To that end, given a compact set and a robust control Lyapunov function satisfying Definition 3.6, for each , define
(50) | |||||
(51) |
where, for each and each ,
(58) |
and the feedback law pair
induces (strong) forward invariance of as stated in R4) (with ) and R5) (with ) in Section 4. Note that under the conditions in Theorem 5.1, the maps in (39) are lower semicontinuous for every . To be able to make continuous selections at , these maps are further required to be lower semicontinuous for .
Theorem 5.3.
Under the conditions of Theorem 5.1 and when , if there exists a feedback law pair , such that R4) and R5) in Section 4 hold666With and ., and
-
M1)
The set-valued map in (50) is lower semicontinuous at each ;
-
M2)
The set-valued map in (51) is lower semicontinuous at each ;
hold, then the state-feedback law pair
defined as
(61) | |||
(62) |
renders the compact set -robustly uniformly globally asymptotically stable for . Furthermore, if the set-valued maps and have closed graph and , then and are continuous.
We revisit our running example and synthesize a stabilizing feedback. Simulations validate the results.
Example 5.4.
(controlled pendulum with impacts (revisited)) From the constructions of and in Example 4.2, the set-valued map is given by
(63) |
for each .
Proceeding in the same way, the set-valued map is given by
for each , where we dropped the term since on we have that .
Now, we synthesize the control law using Theorem 5.3. Defining , , and , the map in (63) can be rewritten as
for each . To determine the pointwise minimum norm control selection according to (46), note that, when , the pointwise minimum norm control selection is and that, when , is given by
which leads to . Then, the pointwise minimum norm control selection is given by777See [FreemanKokotovic96, Chapter 4].
on . Note that there is no division by zero in the construction of since, when we have that implies that , in which case, is defined as zero.
Next, we design the state-feedback law to be used at jumps. According to (47), since maps to , to , and , the pointwise minimum norm control selection is given by
for each . Since , the selection above uniquely defines the input .
Figures 1-4 show closed-loop trajectories using the designed pointwise minimum norm control law . The restitution function used is linear with and , and the function is constant and equal to . The simulation results show convergence to the set , even under the presence of perturbations. For simplicity, the simulations are performed under constant disturbances , for different values of and .
The plots in Figure 1 and Figure 2 correspond to solutions for different values of and with . The velocity component jumps at the impact time and then rapidly gets close to nearby zero. The larger the disturbance, the longer it takes for the solutions to converge. While not being part of the design procedure, the control law steers the solutions to the origin from within the flow set. In fact, as the solutions approach a neighborhood of , they evolve nearby the manifold , which leads to large input values.
The plots in Figure 3 and Figure 4 correspond to solutions for different values of and with . Since the disturbance is positive and captures the uncertainty in the restitution coefficient function, large values of the disturbance cause large peaks after every jump as well as more jumps during the transient, when compared to the results in Figure 1 and Figure 2. After a few jumps, the solutions approach a neighborhood of along the manifold .
6 Conclusion
For a wide class of hybrid systems given in terms of hybrid inclusions with inputs and disturbances, we presented CLF-based results to guarantee the existence of stabilizing state-feedback controllers and to constructively design them. When a CLF is available and the required conditions hold, a state-feedback law with pointwise minimum norm can be constructed to asymptotically stabilize a compact set with robustness to disturbances. A remarkable feature of this controller construction is that it guarantees -robust asymptotic stability of the closed-loop system for any admissible disturbance taking values from (the components of) points in the flow set or jump set. Such disturbances can indeed be large, unlike the disturbances allowed in our previous nominal robustness results in [Goebel.ea.11], and, as a difference to input-to-state stability-based results (see [Cai.Teel.09]), at times can be fully rejected.
The implementation of the proposed feedback laws requires careful treatment to allow for computation in realistic systems. In particular, the computations involved in determining the minimizers in the state-feedback laws (46) and (47) require a nonzero amount of time to terminate. A sample-and-hold or event-triggered implementation of such laws would require variables that trigger the computation events, allow the computations to terminate, and upon termination of the computations, update the inputs to the hybrid system under control. Recent results suggest that, as long as the time for the computations to terminate can be made sufficiently small, it is possible to implement such laws while preserving the stability properties semiglobally and practically [Sanfelice.16.ACC]. Handling the challenges in performing such computations is part of current research efforts.
Finally, the proposed state-feedback law with pointwise minimum norm is expected to also induce an optimality property of the closed-loop system. Using inverse optimality ideas, the robust stabilization problem solved in this paper can be recast as a two-player zero-sum hybrid dynamical game. Under appropriate assumptions, we conjecture that the proposed control law suboptimally solves such hybrid game with a meaningful cost function.