Xian-Ming Zhang

New Lyapunov–Krasovskii Functionals for Global Asymptotic Stability of Delayed Neural Networks

This brief deals with the problem of global asymptotic stability for a class of delayed neural networks. Some new Lyapunov-Krasovskii functionals are constructed by nonuniformly dividing the delay interval into multiple segments, and choosing proper functionals with different weighting matrices corresponding to different segments in the Lyapunov-Krasovskii functionals. Then using these new Lyapunov-Krasovskii functionals, some new delay-dependent criteria for global asymptotic stability are derived for delayed neural networks, where both constant time delays and time-varying delays are treated. These criteria are much less conservative than some existing results, which is shown through a numerical example.

Event-based H ∞ filtering for sampled-data systems

This paper is concerned with event-based H ∞ filtering for sampled-data systems. First, an event-based data packet processor is introduced to release sampled measurement outputs only if an event condition is violated. As a result, communication resources can be saved significantly while preserving the desired H ∞ performance. Second, the resulting filtering error system is modeled as a system with an interval time-varying delay. By employing the Lyapunov-Krasovskii functional approach, a new bounded real lemma (BRL) is established such that the filtering error system is asymptotically stable with the prescribed H ∞ performance. Third, by performing an invertible linear transformation on the filtering error system, a linear matrix inequality (LMI)-based sufficient condition, which is equivalent to the condition in the BRL, is obtained on the feasibility of the event-based H ∞ filtering problem. Consequently, suitable H ∞ filters and the event parameters in the event condition can be co-designed provided that a set of LMIs are satisfied. Finally, a mechanical system with two masses and two springs is given to show the effectiveness of the proposed method.

Global Asymptotic Stability for a Class of Generalized Neural Networks With Interval Time-Varying Delays

This paper is concerned with global asymptotic stability for a class of generalized neural networks (NNs) with interval time-varying delays, which include two classes of fundamental NNs, i.e., static neural networks (SNNs) and local field neural networks (LFNNs), as their special cases. Some novel delay-independent and delay-dependent stability criteria are derived. These stability criteria are applicable not only to SNNs but also to LFNNs. It is theoretically proven that these stability criteria are more effective than some existing ones either for SNNs or for LFNNs, which is confirmed by some numerical examples.

Survey on Recent Advances in Networked Control Systems

Networked control systems (NCSs) are systems whose control loops are closed through communication networks such that both control signals and feedback signals can be exchanged among system components (sensors, controllers, actuators, and so on). NCSs have a broad range of applications in areas such as industrial control and signal processing. This survey provides an overview on the theoretical development of NCSs. In-depth analysis and discussion is made on sampled-data control, networked control, and event-triggered control. More specifically, existing research methods on NCSs are summarized. Furthermore, as an active research topic, network-based filtering is reviewed briefly. Finally, some challenging problems are presented to direct the future research.

Sliding mode control with mixed current and delayed states for offshore steel jacket platforms

This paper is concerned with active control for an offshore steel jacket platform subjected to wave-induced force and parameter perturbations. An uncertain dynamic model for the offshore platform is first established, where uncertainties not only on the natural frequency and the damping ratio of both the offshore platform and the active tuned mass damper (TMD) but also on the damping and stiffness of the TMD are considered. Then, by intentionally introducing a proper time delay into the control channel, a novel sliding mode control scheme is proposed. This scheme uses information about mixed current and delayed states. It is shown through simulation results that this scheme is more effective in both improving the control performance and reducing control force of the offshore platform than some existing ones, such as delay-free sliding mode control, nonlinear control, dynamic output feedback control, and delayed dynamic output feedback control. Furthermore, it is shown that the introduced time delay in this scheme can take values in different ranges while the corresponding control performance of the offshore platform is almost at the same level.

Global asymptotic stability analysis for delayed neural networks using a matrix-based quadratic convex approach.

This paper is concerned with global asymptotic stability for a class of generalized neural networks with interval time-varying delays by constructing a new Lyapunov-Krasovskii functional which includes some integral terms in the form of ∫(t-h)(t)(h-t-s)(j)ẋ(T)(s)Rjẋ(s)ds(j=1,2,3). Some useful integral inequalities are established for the derivatives of those integral terms introduced in the Lyapunov-Krasovskii functional. A matrix-based quadratic convex approach is introduced to prove not only the negative definiteness of the derivative of the Lyapunov-Krasovskii functional, but also the positive definiteness of the Lyapunov-Krasovskii functional. Some novel stability criteria are formulated in two cases, respectively, where the time-varying delay is continuous uniformly bounded and where the time-varying delay is differentiable uniformly bounded with its time-derivative bounded by constant lower and upper bounds. These criteria are applicable to both static neural networks and local field neural networks. The effectiveness of the proposed method is demonstrated by two numerical examples.

An Overview and Deep Investigation on Sampled-Data-Based Event-Triggered Control and Filtering for Networked Systems

This paper provides an overview and makes a deep investigation on sampled-data-based event-triggered control and filtering for networked systems. Compared with some existing event-triggered and self-triggered schemes, a sampled-data-based event-triggered scheme can ensure a positive minimum inter-event time and make it possible to jointly design suitable feedback controllers and event-triggered threshold parameters. Thus, more attention has been paid to the sampled-data-based event-triggered scheme. A deep investigation is first made on the sampled-data-based event-triggered scheme. Then, recent results on sampled-data-based event-triggered state feedback control, dynamic output feedback control,

Abel lemma-based finite-sum inequality and its application to stability analysis for linear discrete time-delay systems

H_\infty

New Globally Asymptotic Stability Criteria for Delayed Cellular Neural Networks

filtering for networked systems are surveyed and analyzed. An overview on sampled-data-based event-triggered consensus for distributed multiagent systems is given. Finally, some challenging issues are addressed to direct the future research.

A Decentralized Event-Triggered Dissipative Control Scheme for Systems With Multiple Sensors to Sample the System Outputs

This paper is concerned with stability of linear discrete time-delay systems. Note that a tighter estimation on a finite-sum term appearing in the forward difference of some Lyapunov functional leads to a less conservative delay-dependent stability criterion. By using Abel lemma, a novel finite-sum inequality is established, which can provide a tighter estimation than the ones in the literature for the finite-sum term. Applying this Abel lemma-based finite-sum inequality, a stability criterion for linear discrete time-delay systems is derived. It is shown through numerical examples that the stability criterion can provide a larger admissible maximum upper bound than stability criteria using a Jensen-type inequality approach and a free-weighting matrix approach.