Research Article | Open Access
Delay-Dependent Robust Performance for Uncertain Neutral Systems with Mixed Time-Varying Delays and Nonlinear Perturbations
This paper deals with the problems of delay-dependent stability and performance for uncertain neutral systems with time-varying delays, and nonlinear perturbations. The time-varying delays are neutral, discrete, and distributed time-varying delays that the upper bounds for the delays are available. The restrictions on the derivatives of the discrete and distributed time-varying delays are removed, which mean that a fast discrete time-varying delay is allowed. The uncertainties under consideration are nonlinear time-varying parameter perturbations and norm-bounded uncertainties, respectively. Firstly, by applying a novel Lyapunov-Krasovskii functional approach, Wirtinger-based integral inequality, Peng-Park’s integral inequality, decomposition technique of constant matrix, descriptor model transformation, Leibniz Newton formula and utilization of zero equation, and improved delay-dependent bounded real lemmas (BRL) for systems are established in terms of linear matrix inequalities (LMIs). Then, based on the obtained BRL, some less conservative delay-dependent stability criteria of uncertain neutral systems with mixed time-varying delays and nonlinear perturbations are obtained and improved performance criterion with the framework of LMIs is introduced. Finally, some numerical examples are given to illustrate that the presented method is effective.
Time delay is frequently a source of instability and a source of generation of oscillation in many dynamic systems such as hybrid systems (and practical application) [1, 2], networked control systems, biological systems, mechanical systems, and chemical or process control systems . Thus, analysis and synthesis problem for systems with time-varying delay have become an important issue and large varieties of problems have been researched since the nineties by several researchers [4–8], performance [9–11].
In some physical system, the system models can be described by functional differential equation of neutral type, in which the models depend on the state delay but also depend on the state derivatives, are often encountered in various fields, such as population ecology , distributed networks containing lossless transmission lines , heat exchangers, and robots in contact with rigid environments . For interesting research methods, stability criteria for application neutral stochastic systems and neural networks have been discussed in [15–19]. On the one hand, some LMI criteria on robust stability for uncertain ones have been deeply derived in [20–23]. Very recently, improved performance analysis and stability for uncertain systems with time-varying delays were proposed in [10, 11, 24]. However, there are rooms for further improvements in the feasible region of criteria for performance and stability.
Stability criteria for time-delay systems are generally divided into two classes: delay-independent one and delay-dependent one. Delay-independent stability criteria tend to be more conservative, especially for small size delay, and such criteria do not give any information on the size of the delay. On the other hand, delay-dependent stability criteria are concerned with the size of the delay and usually provide a maximal delay size. Generally speaking, the latter ones are less conservative than the former ones when the time-delay values are small. Much time and efforts have been put into the development of some techniques and new Lyapunv-Krasovskii functional because how to choose Lyapunov-Krasovskii functional and estimate an upper bound of time-derivative of Lyapunov-Krasovskii functional play key roles to improve the feasible region of stability criteria. Delay-dependent stability criteria for these systems are established in terms of linear matrix inequalities (LMIs).
With above motivations, based on Lyapunov stability theory, improved performance criteria and stability analysis for uncertain neutral systems with mixed time-varying delays and nonlinear perturbations are derived by the framework of LMIs which will be introduced in Theorem 11. Some numerical examples are given to illustrate that the presented method is effective.
2. Problem Formulation
We introduce some notations and lemmas that will be used throughout the paper. denotes the set of all real nonnegative numbers; denotes the -dimensional space with the vector norm ; denotes the Euclidean vector norm of ; denotes the set real matrices; denotes the transpose of the matrix ; is symmetric if ; denotes the identity matrix; matrix is called semipositive definite () if , for all ; is positive definite () if for all ; matrix is called seminegative definite () if , for all ; is negative definite () if for all ; means ; means ; denotes the space of all continuous vector functions mapping into when , ; ; represents the elements below the main diagonal of a symmetric matrix.
Consider the following uncertain neutral system with mixed time-varying delays of the form:where is the state variable, is the disturbance input that belongs to , is the controlled output, is the neutral time-varying delay, and is the discrete and distributed time-varying delays satisfyingwhere and are positive real constants. is initial condition function and , , , and are uncertain matrices. The uncertainties , , and represent the nonlinear parameter perturbations with respect to the current state , the delayed state , and the neutral delayed state , respectively, and are bounded in magnitudewhere and are given positive real constants. We assume that the uncertainties are norm-bounded and can be described aswhere , , , , , , , , , , , , and are real constant matrices with appropriate dimensions. The uncertain matrix satisfiesand is said to be admissible, where is known matrix satisfyingThe uncertain matrix satisfies
Definition 1. Given a scalar , the system (1) is said to be asymptotically stable with the performance level , if it is asymptotically stable and satisfies the -norm constraint , for all nonzero under zero initial condition.
Lemma 3 (Jensen’s inequality). For any symmetric positive definite matrix , positive real number , and vector function such that the following integral is well defined, then
Lemma 4 (Wirtinger-based integral inequality, ). For any matrix , the following inequality holds for all continuously differentiable functions :where and
Lemma 6 (see ). For a positive matrix , the following inequality holds:
Lemma 7 (see ). For a positive matrix , the following inequality holds:
Lemma 10 (see ). Let be a vector-valued function with first-order continuous-derivative entries. Then, the following integral inequality holds for any constant matrices , and is discrete time-varying delays with (3):where
3. Main Results
In this section, a new performance and stability analysis for the system (1) will be introduced by using the Lyapunov functional method combining with linear matrix inequality technique. Firstly, we will establish a new version of delay-dependent BRL for the nominal system (1). We can rewrite the nominal system (1) as follows:We introduce the following notations for later use:where , and other terms are 0.where , exceptand other terms are 0.
Theorem 11. For a prescribed scalar , given scalars and , the system (26) is asymptotically stable with the performance ; if , there exist positive definite symmetric matrices , , , , and , , , any appropriate dimensional matrices, , , , , , , and , , , and positive real constants , , and satisfying the following LMIs:
Proof. Under the conditions of the theorem, we first show the asymptotic stability of the system (26). To this end, we consider the nominal system (26) with , that is,From model transformation method, we rewrite the system (36) in the following system:In order to improve of the discrete delay in (26), let us decompose constant matrices and aswhere , , , and are real constant matrices. By utilizing the zero equations, we obtainwhere and will be chosen to guarantee the asymptotic stability of the system (26). By (39)-(42), the systems (37)-(38) can be represented by the formConstruct a Lyapunov-Krasovskii functional candidate for the systems (43)-(44) of the formwhere with The time derivative of along the trajectory of systems (43)-(44) is given byIt is noted that EP is really . Then the time derivative of is calculated as Differentiating , we have Using Lemmas 8 and 10, is calculated as