Decentralized Adaptive NN Output-Feedback Fault Compensation Control of Nonlinear Switched Large-Scale Systems With Actuator Dead-Zones

Abstract

The problem of adaptive decentralized fault-tolerant control is investigated in this paper for the pure-feedback nonlinear switched large-scale systems. The considered systems possess the unknown compounded nonlinearities (unknown nonlinear functions, unknown interconnected terms, unknown nonlinear function faults, and unknown actuator dead-zone nonlinearity). The radial basis function neural networks are utilized for identifying the unknown compounded nonlinear functions so that the problems of unknown nonlinearities can be solved. A switched neural networks ${k}$ -filter observer is constructed to estimate the unmeasurable states, deal with the unknown actuator dead-zone nonlinearity and compensate the nonlinear function faults simultaneously. Then, by combining the average dwell time theory, adaptive decentralized control method, and under the framework of backstepping design, an adaptive output-feedback fault compensation control method is presented. Under the certain assumptions, the stability of the closed-loop system is proved by use of Lyapunov function stability theory. Finally, the applicability of the proposed controller is well carried out by a double-inverted-pendulums simulation example.