Small-gain stability conditions for linear systems with time-varying delays

In this paper we show that a variety of stability conditions, both existing and new, can be derived for linear systems subject to time-varying delays in a unified manner in the form of scaled small-gain conditions. From a robust control perspective, our development seeks to cast the stability problem as one of robust stability analysis, and the resulting stability conditions are also reminiscent of robust stability bounds typically found in robust control theory. The development is built on the well-known conventional robust stability analysis, requiring essentially no more than a straightforward application of the small gain theorem. The derived conditions have conceptual appeal, and they can be checked using standard robust control toolboxes.     

具有双通道数据包丢失的Delta算子系统故障检测

研究同时存在双通道数据包丢失和时变时延的Delta算子网络控制系统（NCSs）故障检测问题.假定数据包丢失发生在控制器到执行器、传感器至控制器的数据传输过程中，并且利用两个相互独立的伯努利随机变量描述是否发生丢包.将上述的NCSs建模为网络切换系统，提出任意切换律下故障检测滤波器的设计方法.利用线性矩阵不等式（LMIs）方法、Lyapunov-Krasovskii泛函和平均驻留时间等得出所考虑的网络切换系统具备指数均方稳定性的充分条件.证明了所用的网络切换系统满足H_∞性能，并推导出了滤波器参数的显式表达.数值仿真结果验证了所提方法的有效性.     

Robust Stability of Networked Load Frequency Control Systems with Time-Varying Delays

In deregulated power system scenario coupled with smart grid technology, for networked load frequency control (LFC), an open communication structure, owing to low cost and flexibility, is preferred over dedicated networks in the feedback control loop for transmitting/receiving the data between the geographically displaced power system and the control center. In such a control scheme, closing the feedback loop through an open communication channel, in turn, introduces two additive time-varying delays of dissimilar characteristics in the feedback path. These delays degrade the performance of the closed-loop system, and exert a destabilizing effect on the overall system. In this paper, using Lyapunov-Krasovskii functional approach, a less conservative stability criterion is presented to ascertain delay-dependent stability of such network-controlled LFC systems with two additive time-varying delays in the feedback path in the presence of uncertain load disturbance conditions. Unlike the existing results, which are derived by combining these two delays into one, the proposed result considers the two delays as separate entities thereby imparting more generalization into the stability analysis. The effect of unknown exogenous load disturbance is incorporated by mathematically modeling them as a bounded non-linear time-varying function of current and delayed state vectors.     

Event-triggered controller design of nonlinear discrete-time networked control systems in T-S fuzzy model

This article is concerned with event-triggered fuzzy control design for a class of discrete-time nonlinear networked control systems (NCSs) with time-varying communication delays. Firstly, a more general mixed event-triggering scheme (ETS) is proposed. Secondly, considering the effects of the ETS and communication delays, based on the T-S fuzzy model scheme and time delay system approach, the original nonlinear NCSs is reformulated as a new event-triggered networked T-S fuzzy systems with interval time-varying delays. Sufficient conditions for uniform ultimately bound (UUB) stability are established in terms of linear matrix inequalities (LMIs). In particular, the quantitative relation between the boundness of the stability region and the triggering parameters are studied in detail. Thirdly, a relative ETS is also provided, which can be seen as a special case of the above proposed mixed ETS. As a difference from the preceding results, sufficient conditions on the existence of desired fuzzy controller are derived to ensure the asymptotic stability of the closed-loop system with reduced communication frequency between sensors and controllers. Moreover, a co-design algorithm for simultaneously determining the gain matrices of the fuzzy controller and the triggering parameters is developed. Finally, two illustrative examples are presented to demonstrate the advantage of the proposed ETS and the effectiveness of the controller design method.     

Further results on stochastic admissibility for singular Markov jump systems using a dissipative constrained condition

In this paper, the stochastic admissibility analysis problem is investigated for a class of singular Markov jump delayed systems (SMJDSs). The purpose is to establish some conditions such that the SMJDSs are stochastically admissible and strictly (X, Y, Z)-μ-dissipative. A mode-dependent Lyapunov functional and some novel inequalities are proposed for the considered SMJDSs. Based on these, some new stochastic admissibility conditions are established. In comparison with the existing results, the advantage of the obtained conditions lies in the fact that they are not only less conservatism but also more general. Four numerical examples borrowed from some existing papers are given to demonstrate the effectiveness and the reduced conservatism of the presented method.     

A probabilistic approach to stability and stabilization of networked control systems

In this paper, a probabilistic approach is proposed to study the modeling, stability, and stabilization problems for networked control systems (NCSs) with simultaneous random network communication delay and data dropout. Different from existing modeling approaches, the proposed NCSs model naturally captures the stochastic characteristics of the considered NCSs by taking into account the effects of the random network communication delay and data dropout. Based on the developed NCSs model, necessary and sufficient conditions of stability analysis, state, and output feedback stabilization problems are tackled. Moreover, necessary and sufficient stability and stabilization conditions are also presented for the case when the occurrence probabilities of delay and data dropout are partially known. Finally, three numerical examples are provided to demonstrate the effectiveness and the reduced conservatism of the proposed method. Copyright © 2014 John Wiley & Sons, Ltd.     

Improved asymptotic stability conditions for neural networks with discrete and distributed delays

This paper considers the asymptotic stability problem for a class of neural networks with discrete and distributed delays. Based on a new augmented Lyapunov functional and integral inequalities, the new asymptotic stability condition is established in terms of linear matrix inequality. Meanwhile, the importance of some augmented terms in the Lyapunov functional are discussed. Compared with previous methods to deal with the distributed delay, our method is less conservative due to the use of the new Lyapunov functional. Finally, numerical examples illustrate the relaxation of obtained results and our claims.     

Adaptive neural dynamic surface control of MIMO nonlinear time delay systems with time‐varying actuator failures

In this paper, an adaptive dynamic surface control approach is developed for a class of multi‐input multi‐output nonlinear systems with unknown nonlinearities, bounded time‐varying state delays, and in the presence of time‐varying actuator failures. The type of the considered actuator failure is that some unknown inputs may be stuck at some time‐varying values where the values, times, and patterns of the failures are unknown. The considered actuator failure can cover most failures that may occur in actuators of the systems. With the help of neural networks to approximate the unknown nonlinear functions and combining the dynamic surface control approach with the backstepping design method, a novel control approach is constructed. The proposed design method does not require a priori knowledge of the bounds of the unknown time delays and actuator failures. The boundedness of all the closed‐loop signals is guaranteed, and the tracking errors are proved to converge to a small neighborhood of the origin. The proposed approach is employed for a double inverted pendulums benchmark as well as a chemical reactor system. The simulation results show the effectiveness of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd.     

Adaptive neural control for nonstrict‐feedback stochastic nonlinear time‐delay systems with input and output constraints

This paper investigates an adaptive neural tracking control for a class of nonstrict‐feedback stochastic nonlinear time‐delay systems with input saturation and output constraint. First, the Gaussian error function is used to represent a continuous differentiable asymmetric saturation model. Second, the appropriate Lyapunov‐Krasovskii functional and the property of hyperbolic tangent functions are used to compensate the time‐delay effects, the neural network is used to approximate the unknown nonlinearities, and a barrier Lyapunov function is designed to ensure that the output parameters are restricted. At last, based on Lyapunov stability theory, a robust adaptive neural control method is proposed, and the designed controller decreases the number of learning parameters and thus reduces the computational burden. It is shown that the designed neural controller can ensure that all the signals in the closed‐loop system are 4‐Moment (or 2 Moment) semi‐globally uniformly ultimately bounded and the tracking error converges to a small neighborhood of the origin. Two examples are given to further verify the effectiveness of the proposed approach.     

Adaptive neural dynamic surface control of MIMO stochastic nonlinear systems with unknown control directions

In this paper, an adaptive neural output‐feedback control approach is considered for a class of uncertain multi‐input and multi‐output (MIMO) stochastic nonlinear systems with unknown control directions. Neural networks (NNs) are applied to approximate unknown nonlinearities, and K‐filter observer is designed to estimate unavailable system's states. Due to utilization of Nussbaum gain function technique in the proposed approach, the singularity problem and requirement to prior knowledge about signs of high‐frequency gains are removed, simultaneously. Razumikhin functional method is employed to deal with unknown state time‐varying delays, so that the offered control approach is free of common assumptions on derivative of time‐varying delays. Also, an adaptive neural dynamic surface control is developed; hence, explosion of complexity in conventional backstepping method is eliminated, effectively. The boundedness of all the resulting closed‐loop signals is guaranteed in probability; meanwhile, convergence of the tracking errors to adjustable compact set in the sense of mean quartic value is also proved. Finally, simulation results are shown to verify and clarify efficiency of the offered approach. Copyright © 2016 John Wiley & Sons, Ltd.