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. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Abdullah, R., Hussain, A., & Zayed, A. (2005). A new RBF neural network based non-linear self-tuning pole-zero placement controller. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3697 LNCS, pp. 351–357). https://doi.org/10.1007/11550907_56
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