Rongni Yang

Technical communique: Network-based feedback control for systems with mixed delays based on quantization and dropout compensation

This paper deals with the problem of feedback control for networked systems with discrete and distributed delays subject to quantization and packet dropout. Both a state feedback controller and an observer-based output feedback controller are designed. The infinite distributed delay is introduced in the discrete networked domain for the first time. Also, it is assumed that system state or output signal is quantized before being communicated. Moreover, a compensation scheme is proposed to deal with the effect of random packet dropout through communication network. Sufficient conditions for the existence of an admissible controller are established to ensure the asymptotical stability of the resulting closed-loop system. Finally, a numerical example is given to illustrate the proposed design method in this paper.

Filtering for Discrete-Time Networked Nonlinear Systems With Mixed Random Delays and Packet Dropouts

In this technical note, a new class of discrete-time networked nonlinear systems with mixed random delays and packet dropouts is introduced, and the H∞ filtering problem for such systems is investigated. The mixed stochasitc time-delays consist of both discrete and infinite distributed delays and the packet dropout phenomenon occurs in a random way. Furthermore, new techniques are presented to deal with the infinite distributed delay in the discrete-time domain. Sufficient conditions for the existence of an admissible filter are established, which ensure the asymptotical stability as well as a prescribed H∞ performance. Finally, examples are given to demonstrate the effectiveness of the proposed filter design scheme in this technical note.

Predictive Output Feedback Control for Networked Control Systems

This paper studies the problem of predictive output feedback control for networked control systems (NCSs) with random communication delays. A networked-predictive-control scheme is employed to compensate for the network-induced delay. Furthermore, the time-varying predictive controller with mixed random delays for networked systems is introduced. Then, the system is formulated as a Markovian jump system. New techniques are presented to deal with the distributed delay in the discrete-time domain. Based on the analysis of closed-loop NCSs, the designed predictive time-varying output feedback controller can guarantee system stability. Simulation example demonstrates the compensation for random communication delays and data loss in networked systems using the proposed predictive scheme.

Exponential Stability on Stochastic Neural Networks With Discrete Interval and Distributed Delays

This brief addresses the stability analysis problem for stochastic neural networks (SNNs) with discrete interval and distributed time-varying delays. The interval time-varying delay is assumed to satisfy 0 < d₁ ¿ d(t) ¿ d₂ and is described as <i>d</i>(<i>t</i>) = <i>d</i> ₁+<i>h</i>(<i>t</i>) with 0 ¿ <i>h</i>(<i>t</i>) ¿ <i>d</i> ₂ - <i>d</i> ₁. Based on the idea of partitioning the lower bound <i>d</i> ₁, new delay-dependent stability criteria are presented by constructing a novel Lyapunov-Krasovskii functional, which can guarantee the new stability conditions to be less conservative than those in the literature. The obtained results are formulated in the form of linear matrix inequalities (LMIs). Numerical examples are provided to illustrate the effectiveness and less conservatism of the developed results.

Novel Robust Stability Criteria for Stochastic Hopfield Neural Networks With Time Delays

In this paper, the problem of asymptotic stability for stochastic Hopfield neural networks (HNNs) with time delays is investigated. New delay-dependent stability criteria are presented by constructing a novel Lyapunov-Krasovskii functional. Moreover, the results are further extended to the delayed stochastic HNNs with parameter uncertainties. The main idea is based on the delay partitioning technique, which differs greatly from most existing results and reduces conservatism. Numerical examples are provided to illustrate the effectiveness and less conservativeness of the developed techniques.

Stability analysis and stabilization of 2-D switched systems under arbitrary and restricted switchings

In this paper, the problems of stability analysis and stabilization are investigated for discrete-time two-dimensional (2-D) switched systems, which are formulated by the well-known Fornasini-Marchesini local state-space model. Firstly, by using the switched quadratic Lyapunov function approach, a sufficient stability condition is established for such systems under arbitrary switching signal. Then, the extended average dwell time technique combining with the piecewise Lyapunov function approach is developed, and it is utilized for the stability analysis of the 2-D switched systems for the restricted switching case. Based on the stability analysis results, sufficient conditions are presented to stabilize the 2-D switched systems. Finally, two examples are provided to illustrate the effectiveness of the proposed new design techniques.

New Stability Criteria for Neural Networks with Distributed and Probabilistic Delays

This paper is concerned with the stability analysis of neural networks with distributed and probabilistic delays. The probabilistic delay satisfies a certain probability distribution. By introducing a stochastic variable with a Bernoulli distribution, the neural network with random time delays is transformed into one with deterministic delays and stochastic parameters. New conditions for the exponential stability of such neural networks are obtained by employing new Lyapunov–Krasovskii functionals and novel techniques for achieving delay dependence. The proposed conditions reduce the conservatism by considering not only the range of the time delays, but also the probability distribution of their variation. A numerical example is provided to show the advantages of the proposed techniques.

New developments in sliding mode control and its applications 2014

1Space Control and Inertial Technology Research Center, Harbin Institute of Technology, Harbin 150001, China 2School of Control Science and Engineering, Shandong University, Jinan 250061, China 3College of Information and Control Engineering, China University of Petroleum, Qingdao 266555, China 4College of Automation, Harbin Engineering University, Harbin, Heilongjiang 150001, China 5College of Engineering and Science, Victoria University, Melbourne, VIC 8001, Australia

Delay-dependent energy-to-peak filter design for stochastic systems with time delay: A delay partitioning approach

The problem of delay-dependent energy-to-peak filter design for a class of stochastic time-delay systems is investigated in this paper. Attention is focused on the design of full-order and reduced-order filters to guarantee a prescribed energy-to-peak performance for the filtering error system. The improvement lies in that the constructed Lyapunov-Krasovskii functional, based on the delay partitioning technique, can guarantee the obtained delay-dependent conditions to be less conservative than the existing results. The obtained results are formulated in the form of linear matrix inequalities (LMIs), which can be readily solved via standard numerical software. Finally, a numerical example is provided to illustrate the effectiveness and merit of the proposed filter design methods.

Reduced-order filter design of T–S fuzzy stochastic systems with time-varying delay☆

Abstract This paper focuses on the H ∞ reduced-order filter design problem for discrete-time Takagi–Sugeno (T–S) fuzzy delayed systems with stochastic perturbation. Firstly, by using the reciprocally convex method and a novel fuzzy Lyapunov functional, the proposed basis-dependent condition is utilized to guarantee that the filtering error system is mean-square asymptotically stable with a pre-specified H ∞ performance. Then, the corresponding solution of the reduced-order filter model is obtained, which can be transformed into a convex optimization problem by employing the convex linearization approach. Thus, it can be calculated by the standard optimization toolbox. Finally, the advantages and effectiveness of the proposed H ∞ reduced-order filter design technique can be demonstrated by the simulation results, including the inverted pendulum system.