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Discrete Dynamics in Nature and Society

Volume 2014 (2014), Article ID 127185, 6 pages

http://dx.doi.org/10.1155/2014/127185

## Viability Discrimination of a Class of Control Systems on a Nonsmooth Region

^{1}School of Mathematics, Physics and Biological Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China^{2}Department of Applied Mathematics, University of Waterloo, Waterloo, ON, Canada N2L 3G1

Received 27 July 2013; Revised 16 December 2013; Accepted 18 December 2013; Published 12 January 2014

Academic Editor: Zhigang Jiang

Copyright © 2014 Na Zhao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

The viability problem is an important field of study in control theory; the corresponding research has profound significance in both theory and practice. In this paper, we consider the viability for both an affine nonlinear hybrid system and a hybrid differential inclusion on a region with subdifferentiable boundary. Based on the nonsmooth analysis theory, we obtain a method to verify the viability condition at a point, when the boundary function of the region is subdifferentiable and its subdifferential is convex hull of many finite points.

#### 1. Introduction

Hybrid systems have been used to describe complex dynamic systems that involve both continuous and discrete systems. Such hybrid systems can be extensively used in robotics, automated highway systems, air traffic management systems, manufacturing, communication networks, and computer synchronization, and so forth. There has been significant research activity in the area of hybrid systems in the past decade involving researchers from several areas [1–8]. In recent years, the viability of systems is an important research topic; it has been widely used in both reach-ability and designing security domain.

In the study of hybrid systems, the concept of viability is more prevalent. The notion of viability was first introduced by Aubin [9]. Viability property provides a very nice theoretical framework for a hybrid controller design problem. Many researchers have considered the problem of viability for the analysis and control of hybrid systems [10–14]. The nonsampling viability problem was examined in the pioneering work of Aubin and coworkers [10] in which impulse differential inclusions are used to describe hybrid behavior.

As an important part of hybrid system, studies in the viability theory include two topics. One is to verify viability condition for a given set. Another one is to design a viable solution within a viable set. Viability conditions for a linear control system have been studied widely in recent years; see [15, 16]. A necessary and sufficient viability condition for a differential inclusion was given in [8, 17], but it is a hard work to check that condition in most applications directly. In the literature [10], the authors give the necessary and sufficient condition of the viability, but it is still very difficult to judge quantitatively. Gao in [18] discusses the viability discrimination for an affine nonlinear control system on a smooth region; it gives some results on continuous system. There is certain limitation in the application of the literature [18]. The limitation is that the region must be smooth; in fact most of the region’s boundaries are nonsmooth. Ahmed considers the viability criteria for a hybrid differential inclusions on smooth region in [19]. Gao in [20] gives viability criteria for differential inclusions on a nonsmooth region.

In this paper, we mainly consider the viability condition of a hybrid differential inclusion on a region with sub-differentiable boundary. Based on nonsmooth analysis theory, a method for checking the validity of the viability is given for such case as mapping of the set valued at the right hand of the differential inclusion is a polyhedron, the boundary function of the region is sub-differentiable, and its sub-differential is a convex hull with finite point set.

The paper is organized as follows. Section 2 states the main assumption, definitions and describes the hybrid dynamics. Section 3 overcomes these limitations in the literature [18]; we deal with the viability criteria for a hybrid system on a region with sub-differentiable boundary. Section 4 considers the viability of a hybrid differential inclusion. Section 5 shows an example.

#### 2. Preliminaries

Consider the general form of nonlinear control system where denotes the state variable, denotes the control variable, , and is a Lipschitz function which is from to .

*Definition 1 (see [8]). *Let be a subset of , for any initial states , if there exists one solution of the system (1), such that for all ; then we call the subset viable under the system (1); the solution is called viable solution.

*Definition 2 (see [8]). *Let be a nonempty subset of ; the tangent cone of the set at is given by the formula
where is distance from the point to the set .

*Definition 3. *Let be a set valued map, it is said to be upper semicontinuous if for all and each , there exists , such that implies for all ; that is, .

*Definition 4. *Let be a set valued map; is said to be Marchaud if the following conditions hold:(i)is upper semicontinuous;(ii) is a nonempty convex compact set for all ;(iii) is linear growth; that is, there exists , such that
for all .

*Definition 5. *Let be a set valued map, if there exists a constant such that
for all , then is said to be *Lipschitz*, where is a *Lipschitz* constant.

*Definition 6 (see [10], hybrid differential inclusion). *A hybrid differential inclusion is a collection , consisting of a finite dimensional vector space , a set valued map , regarded as a differential inclusion , a set valued map , regarded as a reset map, and a set , regarded as a forced transition set.

*Definition 7 (see [10], run of a hybrid differential inclusion). *A run of a hybrid differential inclusion is a pair , consisting of a hybrid time trajectory and a map , that satisfies:(1)discrete evolution: for all , ;(2)continuous evolution: if , is a solution to the differential inclusion over the interval starting at , with for all .

We use to denote the set of all runs of a hybrid differential inclusion starting at a state .

*Definition 8 (see [10]). *Let be a hybrid differential inclusion. A set is called viable under a hybrid differential inclusion , if for all , there exists an infinite run viable in . is called invariant under the hybrid differential inclusion , if for all , all runs are viable in .

Proposition 9 (see [8]). *The closed set is said to be viable under the system (1), if and only if for any , the following formula is satisfied:
**
For any interior point in the set , the tangent cone , so the above formula is satisfied. Hence, if we want to judge the above formula, we should only consider the boundary point.*

#### 3. The Viability of a Hybrid System

To discuss the problem in , we assume in the following paper.

Consider the following hybrid system , and where and are both Lipschitz functions. is a convex set; it denotes where () are convex functions on the . is a reset map, and is a forced transition set.

Consider the following region : and () are sub-differentiable functions on . Furthermore, we assume that sub-differential is a convex hull of many finite points.

For hybrid time set , where is interval sequence. For , it has , for all . are the points at which discrete transitions take place, are the points after discrete transitions take place; that is,

On the other hand, we assume that the discrete transition does not occur infinite times within the limited time. The set is a forced transition set; that is, the discrete transition must happen for every point in . Without generality, we assume that the set contains the forced transition set and the set contains countable transition points. For discussing easily, we still denote by (). In addition, in order to describe the uncertainy in the hybrid differential system and to determine whether discrete transition will happen for every point in the set we assume that it can prevent the system from death cycle. Obviously, the points which are in may not be jump.

Let Since the point satisfies which is equivalent to so the set can be denoted by the following formula: Because () are sub-differentiable, so is also sub-differentiable; since is a convex hull of many finite points, the sub-differential of is also a convex hull of many finite points, marking Define matrix .

In nonsmooth optimization, two frequently used constraint qualifications: constraint qualification [8]: there exists , such that ; constraint qualification [21]: , where

Lemma 10 (see [14, 21]). *If the set satisfied constraint qualification 1 or constraint qualification 2 at , then .*

According to [20], we get the following Proposition 11 immediately.

Proposition 11 (see [20]). *Assume that constraint qualification 1 or 2 is satisfied; then , where , (), .*

Lemma 12 (see [10]). *Consider a hybrid system such that is Marchaud, is upper semicontinuous with closed domain, and is a closed set. A closed set is viable under if and only if*(1)

*;*(2)

*, .*

*Before we state Theorem 13, we construct the following inequality system:*

*where is a variable.*

According to [20] and Lemma 12, we get the following theorem immediately.

Theorem 13. *For the above hybrid system , if
**
satisfies constraint qualification 1 or 2, then the set is viable under the hybrid system if and only if*(1)*discrete transition (or jump) must take place: , .*(2)*continuous section: for each fixed point inequality system (18) is solvable.*

*Proof. *Under the above assumptions, it is sufficient to show that Theorem 13(1) is equivalent to Lemma 12(1) and Theorem 13(2) is equivalent to Lemma 12(2).

In Lemma 12(1), is equivalent to the following statement: when discrete transition (or jump) must happen for every , then the point after the transition (or jump) must be in the set . Based on the aforementioned assumptions, for the jump point contained in the set , we only need to show that the point will still be in after the jump, . That is, (). Since , (). Hence Theorem 13(1) is equivalent to Lemma 12(1).

The Lemma 12(2) is sufficient to show that the changes is possible for continuous section in , when discrete transition point (or jump point) will be not in after the jump. The set satisfies constraint qualification 1 or 2; then . We set in Proposition 9; then the set is viable under the hybrid system if and only if the following formula is satisfied:
where is a fixed point in . Consider the expressions of the set and ; the above expression is equivalent to
Obviously, the above equation is equivalent to following solvable system:
In (22), we set that substitute into ; then we can obtain (18). Also, we can obtain (22) by substituting into (18). This shows that the system (18) is equivalent to the system (22). This completes the proof.

#### 4. The Viability of a Hybrid Differential Inclusion

Hybrid differential inclusion can describe a hybrid system in a wide range of significance.

Consider the following hybrid differential system , and where () are functions on . is a reset map, and is a forced transition set.

Consider the following region : where () are sub-differentiable functions on . We further assume that the functions are sub-differentiable, and sub-differential is a convex hull with finite point set. Let then the set can be rewritten as Since () are sub-differentiable, is sub-differentiable. Because is a convex hull with finite point set, sub-differential of is also a convex hull with finite point set, denoted by

Theorem 14. *For the above hybrid differential inclusion , if
**
satisfies constraint qualification 1 or 2, then the set is viable under the hybrid differential inclusion if and only if*(1)*discrete transition (or jump) must take place: , ;*(2)*continuous section: Optimal value of the following linear programming problem is zero for each . Consider
* *where .*

*Proof. *Under the above assumptions, it is sufficient to show that Theorem 14(1) is equivalent to Lemma 12(1) and Theorem 14(2) is equivalent to Lemma 12(2).

In Lemma 12(1), is equivalent to the following statement: when discrete transition (or jump) must happen for every , then the point after the transition (or jump) must be in the set (). Based on the aforementioned assumptions, for the jump point in the set , we only need to show that the point after the jump will be still in . That is, (). Since , (). Hence Theorem 14(1) is equivalent to Lemma 12(1).

In Lemma 12(2), we noticed that when discrete transition point (or jump point) after the jump will be not in , then the changes are possible for continuous section in . Since the set satisfies constraint qualification 1 or 2,
In addition,
then the condition
and the following problem which has a solution
are equivalent, and also are equivalent to the linear programming problem in which the optimal solution is zero. This completes the proof.

Lemma 15 (see [10]). *Let hybrid differential inclusion be such that is Marchaud and Lipschitz, and is a closed set. A closed set is invariant under if and only if*(1)

*;*(2)

*, for all .*

Theorem 16. * is a hybrid differential inclusion as above; if the set satisfies constraint qualification 1 or 2, then the set is invariant under hybrid differential inclusion if and only if*(1)*discrete transition (or jump) must take place: , ;* *uncertainty section: , for all ;*(2)*continuous section: , , for all .*

*Proof. *Under the above assumptions, it is sufficient to show that Theorem 16(1) is equivalent to Lemma 15(1) and Theorem 16(2) is equivalent to Lemma 15(2).

In Lemma 15(1), to verify , we just need to show that the point after the transition (or jump) must be in the set , when discrete transition (or jump) must happen for every . By the previous assumptions, the jump point contained in the set should show that the point will be still in after the jump. That is, there exists (), such that (); that is, (). In addition, for each in , the point after the transition (or jump) will still be in ; that is, , . Hence, Theorem 16(1) is equivalent to Lemma 15(1).

In Lemma 15(2), for , is equivalent to the following statement: if the continuous evolution is possible , then all solutions of are all in . The set satisfies constraint qualification 1 or 2; then
Moreover,
then the condition is equivalent to (), which completes the proof of Theorem 16.

#### 5. Example

We provide here an example that better illustrates the class of hybrid systems where our theoretical framework is relevant.

Consider the differential inclusion , where

We can easily conclude that is a sub-differentiable function, and the set is a quarter of the unit circle.(1)Viability discrimination of the point under the hybrid differential inclusion : by the definition and operation of the sub-differential, Obviously, the point is in the set , so discrete transition (or jump) must take place. The point after jump is . It can be shown that the point in and it is the interior point of the set . Hence the point in satisfied the viability condition.(2)Viability discrimination of the point under the hybrid differential inclusion : Obviously, the point is in the set and it is the boundary point. Viability discrimination of the point is equivalent to the following linear programming problem (37) in which the optimal solution is zero We can obtain sub-differential ; hence, , Consequently, the linear programming problem reduces to Obviously, is not the optimal value of constrained optimization. Hence does not satisfy the conditions for the viability of differential inclusion.

#### Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

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