Adaptive Fault-Tolerant Control of Uncertain Nonlinear Large-Scale Systems With Unknown Dead Zone

IEEE Transactions on Cybernetics
Mou Chen, Gang Tao

Abstract

In this paper, an adaptive neural fault-tolerant control scheme is proposed and analyzed for a class of uncertain nonlinear large-scale systems with unknown dead zone and external disturbances. To tackle the unknown nonlinear interaction functions in the large-scale system, the radial basis function neural network (RBFNN) is employed to approximate them. To further handle the unknown approximation errors and the effects of the unknown dead zone and external disturbances, integrated as the compounded disturbances, the corresponding disturbance observers are developed for their estimations. Based on the outputs of the RBFNN and the disturbance observer, the adaptive neural fault-tolerant control scheme is designed for uncertain nonlinear large-scale systems by using a decentralized backstepping technique. The closed-loop stability of the adaptive control system is rigorously proved via Lyapunov analysis and the satisfactory tracking performance is achieved under the integrated effects of unknown dead zone, actuator fault, and unknown external disturbances. Simulation results of a mass-spring-damper system are given to illustrate the effectiveness of the proposed adaptive neural fault-tolerant control scheme for uncertain nonlinea...Continue Reading

References

Sep 24, 2004·IEEE Transactions on Neural Networks·Shuzhi Sam Ge, Cong Wang
Jun 7, 2007·IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society·Zhijun LiAiguo Ming
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May 9, 2014·IEEE Transactions on Neural Networks and Learning Systems·Bin XuZhongke Shi
Oct 21, 2014·IEEE Transactions on Neural Networks and Learning Systems·Chenguang YangBugong Xu
Dec 11, 2014·IEEE Transactions on Neural Networks and Learning Systems·Mou ChenBin Jiang
Apr 8, 2015·IEEE Transactions on Cybernetics·Wei HeZhao Yin

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