A discrete-time system adaptive control using multiple models and RBF neural networks

Authors:
Jun-Yong Zhai;Shu-Min Fei;Kan-Jian Zhang
Affiliations:
Research Institute of Automation, Southeast University, Nanjing, China;Research Institute of Automation, Southeast University, Nanjing, China;Research Institute of Automation, Southeast University, Nanjing, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Year:
2006

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Multiple models adaptive control based on RBF neural network dynamic compensation

ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III

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Abstract

A new control scheme using multiple models and RBF neural networks is developed in this paper. The proposed scheme consists of multiple feedback linearization controllers, which are based on the known nominal dynamics model and a compensating controller, which is based on RBF neural networks. The compensating controller is applied to improve the transient performance. The neural network is trained online based on Lyapunov theory and learning convergence is thus guaranteed. Simulation results are presented to demonstrate the validity of the proposed method.