DocumentCode :
1797801
Title :
Neural-based adaptive integral sliding mode tracking control for nonlinear interconnected systems
Author :
Wen-Shyong Yu ; Chien-Chih Weng
Author_Institution :
Dept. of Electr. Eng., Tatung Univ., Taipei, Taiwan
fYear :
2014
fDate :
6-11 July 2014
Firstpage :
1344
Lastpage :
1351
Abstract :
It is proposed here to use a robust tracking design neural-based adaptive integral sliding mode control technique to control nonlinear interconnected systems with unknown coupled uncertainty in which each uncertainty is assumed to be bounded by an unknown gain. A neural network for nonlinear interconnected systems is then proposed for solving the uncertainties of nonlinear interconnected systems. On-line estimation schemes are developed to overcome the uncertainties and identify the gains of the unknown coupled uncertainty, simultaneously. By the concept of parallel distributed compensation (PDC), we combine adaptive neural scheme the integral sliding mode control scheme to resolve the system uncertainties, unknown coupled uncertainties, and the external disturbances such that H tracking performance is achieved. Simulation results are further presented to show the effectiveness and performance of the proposed control scheme.
Keywords :
adaptive control; compensation; control system synthesis; interconnected systems; neurocontrollers; nonlinear control systems; robust control; uncertain systems; variable structure systems; NN; PDC; adaptive integral sliding mode tracking control; neural network; nonlinear interconnected systems; on-line estimation schemes; parallel distributed compensation; robust tracking design; unknown coupled uncertainty; Adaptive systems; Artificial neural networks; Interconnected systems; Manifolds; Sliding mode control; Uncertainty; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks (IJCNN), 2014 International Joint Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-6627-1
Type :
conf
DOI :
10.1109/IJCNN.2014.6889627
Filename :
6889627
Link To Document :
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