Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 46, Issue 8, Pages 1851-1862Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2015.2456028
Keywords
Adaptive fault control; backstepping control; disturbance observer; large-scale systems; neural network (NN)
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Funding
- Jiangsu Natural Science Foundation of China [SBK20130033]
- National Natural Science Foundation of China [61374130, 61573184]
- Program for New Century Excellent Talents in University of China [NCET-11-0830]
- Six Talents Peak Project of Jiangsu Province [2012-XXRJ-010]
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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 nonlinear large-scale systems.
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