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

Citation

Abdullah R, Hussain A & Zayed AS (2005) A new RBF neural network based non-linear self-tuning pole-zero placement controller. In: Duch W W, Kacprzyk J, Oja E & Zadrozny S (eds.) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005: 15th International Conference, Warsaw, Poland, September 11-15, 2005. Proceedings, Part II. Lecture Notes in Computer Science, 3697. Berlin Heidelberg: Springer, pp. 351-357. http://link.springer.com/chapter/10.1007/11550907_56#; https://doi.org/10.1007/11550907_56

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 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.