Finite-time H_{\mathrm{\infty }} control for a class of Markovian jump systems with mode-dependent time-varying delay

This paper is concerned with the problem of finite-time H_{\mathrm{\infty }} control for a class of Markovian jump system with mode-dependent time-varying delay. By using the new augmented multiple Lyapunov function with more general decomposition approach, a novel sufficient condition for finite-time bounded with an H_{\mathrm{\infty }} performance index is derived. Based on the derived condition, the reliable H_{\mathrm{\infty }} control problem is solved, and an explicit expression of the desired controller is also given, the system trajectory stays within a prescribed bound during a specified time interval. Finally, numerical examples are given to demonstrate that the proposed approach is more effective than some existing ones.

Markovian jump systems were introduced by Krasovskii and Lidskii [1], which can be described by a set of systems with the transitions in a finite mode set. In the last few decades, there has been increasing interest in Markovian jump systems because this class of systems is appropriate to model many physical systems mainly those random failures, repairs and sudden environment disturbance [2–5]. Such class of systems is a special class of stochastic hybrid systems with finite operation modes, which may switch from one to another at different time. As a crucial factor, it is shown that such jumping can be determined by a Markovian chain [6]. For linear Markovian jumping systems, many important issues have been studied extensively such as stability, stabilization, control synthesis and filter design [6–12]. In finite operation modes, Markovian jump systems is a special class of stochastic systems that can switch from one to another at different time. It is worth pointing out that time delay is one of the instability sources for dynamical systems and is a common phenomenon in many industrial and engineering systems [13–18]. Hence, it is not surprising that much effort has been made to investigate of Markovian jump systems with time delay during the last two decades [19–23]. The exponential stabilization of Markovian jump systems with time delay was first studied in [19] where the decay rate was estimated by solving linear matrix inequalities [20]. However, in the aforementioned works, the network-induced delays have been commonly assumed to be deterministic, which is fairly unrealistic since delays resulting from network transmissions are typically time varying.

Generally speaking, the delay-dependent criterions are less conservative than delay-independent ones, especially when the time delay is small enough in Markovian jump systems. Thus, recent efforts were devoted to the delay-dependent Markovian jump systems stability analysis by employing Lyapunov-Krasovskii functionals [24–32]. However, most efforts have been given on how to construct an appropriate Lyapunov functional by dividing the delay interval [-{\mu }_2,-{\mu }_1] into N equal length subintervals [22]. It should be pointed out that the delay decomposition method is not effective when the lower bound of time-varying delay is zero. Furthermore, although the decay rate can be computed, it is a fixed value that one cannot adjust to deduce if a larger decay rate is possible. Therefore, how to obtain the improved results without increasing the computational burden has greatly improved the current study.

Over the years, many research efforts have been devoted to the study of finite-time stability of systems. In finite-time interval, finite-time stability is investigated to address these transient performances of control systems. Recently, the concept of finite-time stability has been revisited in the light of linear matrix inequalities (LMIs) and Lyapunov function theory, some results are obtained to ensure that system is finite-time stable or finite-time bounded [33–49]. It is noted that there are still some related issues to be solved, to the best of our knowledge, the finite-time H_{\mathrm{\infty }} control for a class of Markovian jump systems with time-varying delay has not been fully developed. The analysis method in the existing references seems still conservative to study Markovian jump system. There is room for further investigation.

The main contribution of this paper is as follows: Firstly, we present a new augmented Lyapunov functional by employing the more general decomposition of a delay interval for a class of Markovian jump systems with mode-dependent time-varying delay. Secondly, in order to reduce the possible conservativeness and computational burden, some slack matrices are introduced [18]. Several sufficient conditions are derived to guarantee the finite-time stability and boundedness of the resulting closed-loop system. Last but not the least, it is shown that less conservative and more general results can be derived since the time-varying delays are divided into a more general decomposition. We find that finite-time stability is a concept independent from Lyapunov stability and can always be affected by switching behavior significantly, thus it deserves our investigation. The finite-time boundedness criteria can be tackled in the form of LMIs. Finally, numerical examples illustrate the effectiveness of the developed techniques.

Notations: Throughout this paper, we let P>0 (P\ge 0, , P\le 0) denote a symmetric positive definite matrix P (positive semi-definite, negative definite and negative semi-definite). For any symmetric matrix P, {\lambda }_{max}(P) and {\lambda }_{min}(P) denote the maximum and minimum eigenvalues of matrix P, respectively. {\mathcal{R}}^n denotes the n-dimensional Euclidean space and {\mathcal{R}}^{n\times m} refers to the set of all n\times m real matrices and \mathcal{N}=\{1,2,\dots ,N\}. The identity matrix of order n is denoted as I_n. ∗ represents the elements below the main diagonal of a symmetric matrix. The superscripts ⊺ and −1 stand for matrix transposition and matrix inverse, respectively.

Given a probability space (\mathrm{\Omega },F,P) where Ω, F and P respectively represent the sample space, the algebra of events and the probability measure defined on F. In this paper, we consider the following Markov jump system over the space (\mathrm{\Omega },F,P) described by

where x(t)\in {\mathcal{R}}^n is the state vector of the system, z(t)\in {\mathcal{R}}^q is the controlled output, u(t)\in {\mathcal{R}}^m is the control input and \phi (t), t=[-{\mu }_2,0] and r_0\in \mathcal{N} are initial conditions of continuous state and the mode. \omega (k)\in {\mathcal{R}}^q is the disturbance input satisfying

Let the random form process \{r_t,t\ge 0\} be the Markov stochastic process taking values on a finite set \mathcal{N}=\{1,2,\dots ,N\} with the transition rate matrix \mathrm{\Omega }=\{{\pi }_{ij}\}, i,j\in \mathcal{N}, and the transition probabilities described as

where \mathrm{\Delta }>0, {\pi }_{ij}\ge 0, for i\ne j, is the transition rate from mode i at time t to mode j at time t+\mathrm{\Delta } and

for each mode i\in \mathcal{N}, {lim}_{\mathrm{\Delta }\to 0_{+}}\frac{o(\mathrm{\Delta })}{\mathrm{\Delta }}=0. {\tau }_i(t) denotes the mode dependent time-varying state delay in the system and satisfies the following condition:

where {\mu }_1=min\{{\mu }_{1i},i\in \mathcal{N}\} and {\mu }_2=max\{{\mu }_{2i},i\in \mathcal{N}\} are prescribed integers representing the lower and upper bounds of time-varying delay {\tau }_i(t). Similarly, h_1=min\{h_{1i},i\in \mathcal{N}\} and h_2=max\{h_{2i},i\in \mathcal{N}\} are prescribed integers representing the lower and upper bounds of time-varying delay {\dot{\tau }}_i(t). A_{r_t}, A_{\tau r_t}, B_{r_t}, D_{r_t}, C_{r_t}, C_{\tau r_t}, E_{r_t} and F_{r_t} are known mode-dependent matrices with appropriate dimension functions of the random jumping process \{r_t\} and represent the nominal systems for each r_t\in \mathcal{N}. For notation simplicity, when the system operates in the i th mode (r_t=i), A_{r_t}, A_{\tau r_t}, B_{r_t}, D_{r_t}, C_{r_t}, C_{\tau r_t}, E_{r_t} and F_{r_t} are denoted as A_i, A_{\tau i}, B_i, D_i, C_i, C_{\tau i}, E_i and F_i, respectively.

Remark 1 In this paper, the lower bound of {\dot{\tau }}_i(t) is required in order to implement the proposed delay decomposition method. If h_{1i}=h_{2i}=0, then {\tau }_i(t) corresponds to the constant delay.

Moreover, the transient process of a system can be clearly characterized if its decay rate is available. The objective of this study is to develop a new approach to designing a state feedback controller

via a novel Lyapunov functional such that the resulting closed-loop system is finite-time stable, where K_i is the controller gains to be designed.

In this paper, we split the delay interval [-{\mu }_2,-{\mu }_1] into two segments: [-{\tau }_i(t),-{\mu }_1]\cup [-{\mu }_2,-{\tau }_i(t)]. Moreover, we further subdivide each interval into l, m equal length subsegments [-{\tau }_k,-{\tau }_{k-1}] and [-{\tau }_{l+s},-{\tau }_{l+s-1}], respectively, where

and l, m are given positive integers.

Remark 2 The delay intervals are divided subsegments dependent on t, thus the proposed delay decomposition method is more general than those in [13–17, 19–24]. The conservatism will be reduced with the partitioning number l and m increase.

In order to more precisely describe the main objective, we introduce the following definitions and lemmas for the underlying system.

Definition 2.1 System (1) is said to be finite-time bounded with respect to (c_1,c_2,T,{\overline{R}}_{r_t},d) if condition (2) and the following inequality hold:

where c_2>c_1\ge 0 and {\overline{R}}_{r_t}>0.

Definition 2.2 [49]

Consider V(x_t,r_t) as the stochastic Lyapunov function of the resulting system (1), its weak infinitesimal operator is defined as

Definition 2.3 Given a constant T>0, for all admissible \omega (t) subject to condition (2), under zero initial conditions, if the closed-loop Markovian jump system (1) is finite-time bounded and the control outputs satisfy condition (5) with attenuation \gamma >0,

then the controller system (1) is called the finite-time bounded with disturbance attenuation γ.

Remark 3 It should be pointed that the assumption of zero initial condition in system (1) is only for the purpose of technical simplification in the derivation, and it does not lose generality. In fact, if this assumption is lost, the same control result can still be got along the same lines, except adding extra manipulations in the derivation and extra terms in the control presentation. However, in real world applications, the initial condition of the underlying system is generally not zero.

Lemma 2.1 [18]

Let f_i:{\mathcal{R}}^m\to \mathcal{R} (i=1,2,\dots ,N) have positive values in an open subset of {\mathcal{R}}^m. Then the reciprocally convex combination of f_i over satisfies

subject to

Lemma 2.2 For a given function {\mu }_{1i}\le {\tau }_i(t)\le {\mu }_{2i}, h_{1i}\le {\dot{\tau }}_i(t)\le h_{2i} (i\in \mathcal{N}), there exist four functions {\alpha }_1(t)\ge 0, {\alpha }_2(t)\ge 0, {\beta }_1(t)\ge 0 and {\beta }_2(t)\ge 0 satisfying {\alpha }_1(t)+{\alpha }_2(t)=1 and {\beta }_1(t)+{\beta }_2(t)=1, respectively, such that \mathrm{\forall }i\in \mathcal{N}, the following equation holds:

Lemma 2.3 [50]

For matrices A, Q=Q^{⊺}>0 and P>0, the following matrix inequality

holds if and only if there exists a matrix G of appropriate dimension such that

Lemma 2.4 ([14] Schur complement)

Given constant matrices X, Y, Z, where X=X^{⊺} and , then if and only if

We first consider the problem of stability analysis for system (1) with u(t)=0. The following results actually present the finite-time stability for the Markov jump system with time-varying delay.

Theorem 3.1 System (1) is finite-time bounded with respect to (c_1,c_2,d,{\overline{R}}_i,T) if there exist matrices P_i>0, Q_i^{(r)}>0, Q^{(r)}>0 (r=1,2,\dots ,(m+l)), R_i, R>0, W>0, S_i, \mathrm{\forall }i,j\in \mathcal{N}, scalars , T>0, {\kappa }_1>0, {\kappa }_2>0, {\kappa }_3>0, {\lambda }_s>0 (s=1,2,\dots ,7), \lambda >0, \eta >0 and \mathrm{\Lambda }>0, such that for all i,j\in \mathcal{N}, k=1,2,\dots ,l, s=1,2,\dots ,m, the following inequalities hold:

where

Proof First, in order to cast our model into the framework of the Markov processes, we define a new process \{(x_t,r_t),t\ge 0\} by

Now, we consider the following Lyapunov-Krasovskii functional:

where

Then, let the mode at time t be i, i.e., r_t=i\in \mathcal{N}, we have

Moreover, denote

By using Lemma 2.1, it yields that