Neural network based controller for constrained multivariable systems

Authors:
H. Al-Duwaish;S. Z. Rizvi
Affiliations:
King Fahd University of Petroleum & Minerals, Department of Electrical Engineering, Saudi Arabia;King Fahd University of Petroleum & Minerals, Department of Electrical Engineering, Saudi Arabia
Venue:
ACMOS'10 Proceedings of the 12th WSEAS international conference on Automatic control, modelling & simulation
Year:
2010

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Abstract

This paper presents a new neural network based controller design for multivariable systems. The proposed controller is designed using radial basis function (RBF) neural network. Weight update equation using classical least mean square principle is derived for the RBF network. The controller generates optimal control signals abiding by constraints, if any, on the control signals. Simulation results are included in the end to assess the performance of the proposed controller.