Title :
Adaptive neural network observer based fault-tolerant control for a class of uncertain nonlinear systems
Author :
Meng, Lingya ; Jiang, Bin
Author_Institution :
Coll. of Inf. & Control Eng., China Univ. of Pet., Qingdao, China
Abstract :
This paper presents an adaptive neural network observer based fault-tolerant control approach to a class of uncertain nonlinear system. This approach can not only deal with the unknown nonlinear faults from the actuators, but also from the plant. Moreover, the scheme can be easily implemented in the control engineering by relaxing the fault-tolerant control law. The uniform ultimate boundedness of the fault estimation error vector and the asymptotical stability of the closed-loop fault-tolerant control system are guaranteed by Lyapunov theory. The numerical simulation results demonstrate the application and effectiveness of the proposed fault-tolerant control scheme.
Keywords :
Lyapunov methods; adaptive control; asymptotic stability; closed loop systems; fault tolerance; neurocontrollers; nonlinear control systems; observers; uncertain systems; Lyapunov theory; actuators; adaptive neural network observer; asymptotical stability; closed-loop fault-tolerant control system; control engineering; fault estimation error vector; uncertain nonlinear systems; unknown nonlinear faults; Actuators; Adaptive systems; Fault tolerance; Fault tolerant systems; Nonlinear systems; Observers; Stability analysis;
Conference_Titel :
Intelligent Control and Information Processing (ICICIP), 2011 2nd International Conference on
Conference_Location :
Harbin
Print_ISBN :
978-1-4577-0813-8
DOI :
10.1109/ICICIP.2011.6008435