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

Abdullah, Rudwan; Hussain, Amir; Zayed, Ali

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.

• Presentation Conference Type: Conference Paper (Published)
• Conference Name: ICANN 2005: International Conference on Artificial Neural Networks
• Start Date: Sep 11, 2005
• End Date: Sep 15, 2005
• Publication Date: 2005
• Deposit Date: Oct 17, 2019
• Publisher: Springer
• Pages: 351-357
• Series Title: Lecture Notes in Computer Science
• Series Number: 3697
• Series ISSN: 0302-9743
• Book Title: Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005 15th International Conference, Warsaw, Poland, September 11-15, 2005. Proceedings, Part II
• ISBN: 978-3-540-28755-1
• DOI: https://doi.org/10.1007/11550907_56
• Keywords: Radial Basis Function Neural Network (RBF); self-tuning controller algorithm; non-linear dynamical systems
• Public URL: http://researchrepository.napier.ac.uk/Output/1793653