A novel multiple-controller incorporating a radial basis function neural network based generalized learning model

Zayed, Ali S.; Hussain, Amir; Abdullah, Rudwan A.

Abstract

A new adaptive multiple-controller is proposed incorporating a radial basis function (RBF) neural network based generalized learning model (GLM). The GLM assumes that the unknown complex plant is represented by an equivalent stochastic model consisting of a linear time-varying sub-model plus a non-linear RBF neural-network learning sub-model. The proposed non-linear multiple-controller methodology provides the designer with a choice, through simple switching, of using: either, a conventional proportional-integral-derivative (PID) controller, a PID structure based pole (only) placement controller, or a newly developed PID structure based (simultaneous) zero and pole placement controller. Closed-loop stability analysis of the multiple-controller framework is discussed and sample simulation results using a realistic non-linear single-input single-output (SISO) plant model are used to demonstrate the effectiveness of the multiple-controller with respect to tracking desired set-point changes and dealing with sudden introduction of disturbances.

• Journal Article Type: Article
• Acceptance Date: Feb 9, 2006
• Online Publication Date: Jun 23, 2006
• Publication Date: 2006
• Deposit Date: Oct 16, 2019
• Journal: Neurocomputing
• Print ISSN: 0925-2312
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 69
• Issue: 16-18
• Pages: 1868-1881
• DOI: https://doi.org/10.1016/j.neucom.2006.02.017
• Keywords: Multiple controllers; Learning models; Neural networks; PID control; Zero-pole placement control; Switching
• Public URL: http://researchrepository.napier.ac.uk/Output/1793607