DocumentCode :
3202784
Title :
Robust adaptive neural control of nonlinear systems with dynamic uncertainties and input saturation
Author :
Huanqing Wang ; Wanjing Sun ; Liang Liu
Author_Institution :
Sch. of Math. & Phys., Bohai Univ., Jinzhou, China
fYear :
2015
fDate :
23-25 May 2015
Firstpage :
216
Lastpage :
221
Abstract :
In this paper, the problem of adaptive neural control is considered for a class of strict-feedback nonlinear systems with unmodeled dynamics, dynamic disturbances and unknown input saturation. During the controller design, radial basis functions(RBF) neural networks are applied to model the unknown nonlinearities, and an adaptive neural control scheme is developed via backstepping, which guarantees that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded in mean square. A simulation example is provided to show the effectiveness of the proposed control scheme.
Keywords :
adaptive control; closed loop systems; control nonlinearities; control system synthesis; neurocontrollers; nonlinear control systems; radial basis function networks; robust control; RBF neural networks; backstepping; closed-loop system; controller design; dynamic uncertainties; input saturation; radial basis function neural networks; robust adaptive neural control; strict-feedback nonlinear systems; Adaptation models; Adaptive systems; Backstepping; Closed loop systems; Neural networks; Nonlinear dynamical systems; Adaptive neural control; Backstepping; Input saturation; Unmodeled dynamics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control and Decision Conference (CCDC), 2015 27th Chinese
Conference_Location :
Qingdao
Print_ISBN :
978-1-4799-7016-2
Type :
conf
DOI :
10.1109/CCDC.2015.7161693
Filename :
7161693
Link To Document :
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