This paper is concerned with the input-to-state stabilizing control problem for cyber-physical systems (CPSs) with multiple transmission channels under denial-of-service (DoS) attacks. Under the data update policy with bounded update interval, a new control scheme that discards the outdated information is proposed, and the stability analysis of CPSs under DoS attacks is transformed into analyzing the stability of the system under a switched controller with the help of a class of linear matrix inequalities (LMIs). Then, inspired by the techniques for switched systems, sufficient conditions on the duration and frequency of the DoS attacks, under which the stability of the closed-loop systems is still guaranteed, are proposed. Compared with the existing method for the single-channel case, the considered multiple-channel case is more challenging, and the proposed LMI-based method is more flexible.

Input-to-State Stabilizing Control for Cyber-Physical Systems With Multiple Transmission Channels Under Denial of Service

The problem of active fault-tolerant control (FTC) is investigated for the large-scale nonlinear systems in nonstrict-feedback form. The nonstrict-feedback nonlinear systems considered in this paper consist of unstructured uncertainties, unmeasured states, unknown interconnected terms, and actuator faults (e.g., bias fault and gain fault). A state observer is designed to solve the unmeasurable state problem. Neural networks (NNs) are used to identify the unknown lumped nonlinear functions so that the problems of unstructured uncertainties and unknown interconnected terms can be solved. By combining the adaptive backstepping design principle with the combination Nussbaum gain function property, a novel NN adaptive output-feedback FTC approach is developed. The proposed FTC controller can guarantee that all signals in all subsystems are bounded, and the tracking errors for each subsystem converge to a small neighborhood of zero. Finally, numerical results of practical examples are presented to further demonstrate the effectiveness of the proposed control strategy.

Adaptive Neural Networks Decentralized FTC Design for Nonstrict-Feedback Nonlinear Interconnected Large-Scale Systems Against Actuator Faults

(2008). Stochastic Switching Systems: Analysis and Design. Journal of the American Statistical Association: Vol. 103, No. 481, pp. 430-430.

Stochastic Switching Systems: Analysis and Design

A Takagi-Sugeno (T-S) model is employed to represent a networked control system (NCS) with different network-induced delays. Comparing with existing NCS modeling methods, this approach does not require the knowledge of exact values of network-induced delays. Instead, it addresses situations involving all possible network-induced delays. Moreover, this approach also handles data-packet loss. As an application of the T-S-based modeling method, a parity-equation approach and a fuzzy-observer-based approach for fault detection of an NCS were developed. An example of a two-link inverted pendulum is used to illustrate the utility and viability of the proposed approaches

CORRESPONDENCE - Takagi-Sugeno Fuzzy-Model-Based Fault Detection for Networked Control Systems with Markov Delays

This article devotes to investigating the issue of fuzzy adaptive control for a class of strict-feedback nonlinear systems with nonaffine nonlinear faults. The computational complexity is reduced by adopting the dynamic surface control technique. Under the framework of finite-time stability, a novel fault-tolerant control strategy is designed so that the closed-loop system is semiglobally practically finite-time stable, and the tracking error converges to a small residual set in a finite time. Finally, simulation studies for an electromechanical system are shown to verify the feasibility of the presented approach.

Fuzzy Adaptive Finite-Time Fault-Tolerant Control for Strict-Feedback Nonlinear Systems

Simultaneous fault detection and control for switched linear systems with mode-dependent average dwell-time

Fault detection for stochastic parameter-varying Markovian jump systems with application to networked control systems

Adaptive fuzzy fault-tolerant control with guaranteed tracking performance for nonlinear strict-feedback systems

This study investigates integral sliding mode control problems for Markovian jump T–S fuzzy descriptor systems via the super-twisting algorithm. A new integral sliding surface which is continuous is constructed and an integral sliding mode control scheme based on a variable gain super-twisting algorithm is presented to guarantee the well-posedness of the state trajectories between two consecutive switchings. The stability of the sliding motion is analysed by considering the descriptor redundancy and the properties of fuzzy membership functions. It is shown that the proposed variable gain super-twisting algorithm is an extension of the classical single-input case to the multi-input case. Finally, a bio-economic system is numerically simulated to verify the merits of the method proposed.

/pdf/integral-sliding-mode-control-for-markovian-jump-t-s-fuzzy-5a8qeiykov.pdf

Integral Sliding Mode Control for Markovian Jump T-S Fuzzy Descriptor Systems Based on the Super-Twisting Algorithm

This paper investigates observer-based stabilization for nonlinear descriptor systems using a fuzzy integral sliding mode control approach Observer-based integral sliding mode control strategies for the Takagi–Sugeno (T–S) fuzzy descriptor systems are developed A two-step design approach is first developed to obtain the observer gains and coefficients in the switching function using linear matrix inequalities (LMIs), and the results are used to facilitate the development of a single-step design approach, which is seen to be convenient but introduces some conservatism in the design The potential application to a class of mechanical systems is also considered Since the descriptor system representation of mechanical systems is adopted, it is shown that in contrast to the existing fuzzy sliding mode control methods based on the normal system representation, the resulting T-S fuzzy system does not contain different input matrices for each local subsystem and the required number of fuzzy rules is consequently markedly reduced Finally, the balancing problem of a pendulum on a car is numerically simulated to demonstrate the effectiveness of the proposed method

/pdf/observer-based-fuzzy-integral-sliding-mode-control-for-2phzq4phpj.pdf

Jinghao Li

Papers

Simultaneous fault detection and control for switched linear systems with mode-dependent average dwell-time

Fault detection for stochastic parameter-varying Markovian jump systems with application to networked control systems

Adaptive fuzzy fault-tolerant control with guaranteed tracking performance for nonlinear strict-feedback systems

Integral Sliding Mode Control for Markovian Jump T-S Fuzzy Descriptor Systems Based on the Super-Twisting Algorithm

Observer-Based Fuzzy Integral Sliding Mode Control For Nonlinear Descriptor Systems