A new radial basis function neural network based multi-variable adaptive pole-zero placement controller

Abdullah, R.A.; Hussain, A.; Zayed, A.S.

Abstract

In this paper a new multi-variable adaptive controller algorithm for non-linear dynamical systems has been derived which employs the radial basis function (RBF) neural network. In the proposed controller, the unknown plant is represented by an equivalent model consisting of a linear time-varying sub-model plus a non-linear `learning' sub-model. The parameters of the linear sub-model are identified by a recursive least squares (RLS) algorithm with a directional forgetting factor, whereas the unknown non-linear sub-model is modeled using the RBF neural network resulting in a new multi-variable non-linear controller with a generalized minimum variance performance index. In addition, the new controller overcomes the shortcomings of other linear control designs and provides an adaptive mechanism which ensures that both the closed-loop poles and zeros are placed at their pre-specified positions. Simulation results using a non-linear multi-input multi-output (MIMO) plant model demonstrate the effectiveness of the proposed controller.

• Presentation Conference Type: Conference Paper (Published)
• Conference Name: 2006 IEEE International Conference on Engineering of Intelligent Systems
• Start Date: Sep 18, 2006
• End Date: Apr 23, 2006
• Online Publication Date: Sep 18, 2006
• Publication Date: 2006
• Deposit Date: Oct 16, 2019
• Publisher: Institute of Electrical and Electronics Engineers
• Book Title: 2006 IEEE International Conference on Engineering of Intelligent Systems
• ISBN: 1-4244-0456-8
• DOI: https://doi.org/10.1109/ICEIS.2006.1703158
• Keywords: Multi-variable controllers, RBF neural networks, zero-pole placement control
• Public URL: http://researchrepository.napier.ac.uk/Output/1793598