Adaptive fuzzy decentralized output feedback control for stochastic nonlinear large‐scale systems using DSC technique

Abstract

In this paper, an adaptive fuzzy decentralized backstepping output feedback control approach is proposed for a class of uncertain large-scale stochastic nonlinear systems without the measurements of the states. The fuzzy logic systems are used to approximate the unknown nonlinear functions, and a fuzzy state observer is designed for estimating the unmeasured states. Using the designed fuzzy state observer, and by combining the adaptive backstepping technique with dynamic surface control technique, an adaptive fuzzy decentralized output feedback control approach is developed. It is shown that the proposed control approach can guarantee that all the signals of the resulting closed-loop system are semi-globally uniformly ultimately bounded in probability, and the observer errors and the output of the system converge to a small neighborhood of the origin by choosing appropriate design parameters. A simulation example is provided to show the effectiveness of the proposed approaches.

Finite-horizon reliable control with randomly occurring uncertainties and nonlinearities subject to output quantization

“…So far, considerable research attention has been devoted to the theoretical research on control problems for nonlinear stochastic systems, see [1,3,5,6,9,11,20,22,24,30] and the references therein. For example, for different kinds of nonlinear stochastic systems, the H ∞ output feedback control problem has been investigated in [24], the adaptive fuzzy control problem has been proposed in [22], the neural-network-based controller design has been addressed in [23], the adaptive sliding mode controller has been designed in [4,10,17,18] and the observer-based control problems have been solved in [25], respectively. Among various descriptions of nonlinearities, the so-called randomly occurring nonlinearities (RONs) [26] cater for those randomly changeable nonlinearities in terms of their types and/or intensities governed by stochastic variables.…”

Adaptive fuzzy decentralized tracking fault-tolerant control for stochastic nonlinear large-scale systems with unmodeled dynamics

“…In case of no actuator faults, the nonlinear system (1) has been widely studied in the literature [31,35,46]. In addition, if f i;j ðÁÞ is known, the system (1) becomes the one studied in [21].…”

“…Wang et al [33] proposed an adaptive fuzzy decentralized state feedback control scheme for a class of uncertain stochastic nonlinear large-scale systems. S.C. Tong et al [31,46] studied adaptive fuzzy decentralized output feedback DSC stabilization and robust stabilization problem for a class of uncertain stochastic largescale nonlinear systems with unmodeled dynamics. Despite the great progress has been made for control design of stochastic large-scale nonlinear systems, however, the existing control approaches all assume that all the components of the nonlinear large-scale systems are in good operating conditions, that is, the controlled systems are free of the actuator faults.…”

“…A novel fault detection filter was designed for twodimensional discrete-time Markovian jump systems in [41]. More recently, adaptive control design for stochastic nonlinear systems has received increasing attention [4,11,39,40], and many adaptive backstepping control design methods for deterministic nonlinear large-scale systems have been extended to the case of stochastic nonlinear large-scale systems, see for example [5,21,31,33,35,42,46], and references therein. The authors in [5,21,42] investigated adaptive decentralized output feedback stabilization control design of stochastic nonlinear large-scale systems.…”

Observer-based adaptive neural control for a class of nonlinear pure-feedback systems

“…Recent years, some observer-based adaptive neural/fuzzy control strategies were reported. In [38][39][40][41] output feedback stabilization or tracking control for SISO system using NN/FLS is introduced.…”