1384232

11559

An adaptive nonlinear control strategy based on networks of compactly supported radial basis functions is proposed. The local influence of the basis functions allows efficient on-line adaptation that is performed using a gradient law, and new basis functions are added to the network only when new regions in state space are encountered and the prediction error exceeds a pre-specified tolerance. The approximate model is used to construct an input-output linearizing control law. The adaptive control strategy is applied to a nonlinear chemical reactor model.

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radial basisfunctions , Adaptive control , nonlinear control , Artificial neural networks , Nonlinear identification

Studia Iranica

356

356