Letters: Adaptive RBF neural-networks control for a class of time-delay nonlinear systems

Authors:
Qing Zhu;Shumin Fei;Tianping Zhang;Tao Li
Affiliations:
Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Jiangsu, Nanjing 210096, China and College of Information Engineering, Yangzhou University, Jian ...;Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Jiangsu, Nanjing 210096, China;College of Information Engineering, Yangzhou University, Jiangsu, Yangzhou 225009, China;Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Jiangsu, Nanjing 210096, China
Venue:
Neurocomputing
Year:
2008

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Abstract

A control scheme combined with backstepping, radius basis function (RBF) neural networks and adaptive control is proposed for the stabilization of nonlinear system with input and state delay. By using state transformation, the original system is converted to the system without input delay. The RBF neural network is employed to estimate the unknown continuous function. The controller is designed for the converted system so that the closed-loop system is bounded. According to the relation between the original system and the converted one, the state of the original system is proved to be bounded. The control scheme ensures that the closed-loop system is semi-globally uniformly ultimately bounded.