A new RBF neural network based non-linear self-tuning pole-zero placement controller

  • Authors:

  • Rudwan Abdullah;Amir Hussain;Ali Zayed

  • Affiliations:

  • Department of Computing Science and Mathematics, University of Stirling, Scotland;Department of Computing Science and Mathematics, University of Stirling, Scotland;Department of Computing Science and Mathematics, University of Stirling, Scotland

  • Venue:
  • ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
  • Year:
  • 2005

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Abstract

In this paper a new self-tuning controller algorithm for non-linear dynamical systems has been derived using the Radial Basis Function Neural Network (RBF). In the proposed controller, the unknown non-linear plant is represented by an equivalent model consisting of a linear time-varying submodel plus a non-linear sub-model. The parameters of the linear sub-model are identified by a recursive least squares algorithm with a directional forgetting factor, whereas the unknown non-linear sub-model is modelled using the (RBF) network resulting in a new non-linear controller with a generalised minimum variance performance index. In addition, the proposed controller overcomes the shortcomings of other linear designs and provides an adaptive mechanism which ensures that both the closed-loop poles and zeros are placed at their prespecified positions. Example simulation results using a non-linear plant model demonstrate the effectiveness of the proposed controller.