Research Output
A new RBF neural network based non-linear self-tuning pole-zero placement controller
  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 sub-model 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 pre-specified positions. Example simulation results using a non-linear plant model demonstrate the effectiveness of the proposed controller.

  • Date:

    31 December 2005

  • Publication Status:


  • DOI:


  • Funders:

    Historic Funder (pre-Worktribe)


Abdullah, R., Hussain, A., & Zayed, A. (2005). A new RBF neural network based non-linear self-tuning pole-zero placement controller. In Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005 15th International Conference, Warsaw, Poland, September 11-15, 2005. Proceedings, Part II. , (351-357).



Radial Basis Function Neural Network (RBF); self-tuning controller algorithm; non-linear dynamical systems

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