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Neural network control of multivariable processes with a fast optimisation algorithm

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Abstract

A radial basis function (RBF) neural network model based predictive control scheme is developed for multivariable nonlinear systems in this paper. A fast convergence algorithm is proposed and employed in multidimensional optimisation in the control scheme to reduce the computing time and save required computer memory. The scheme is applied to a simulated two-input two-output nonlinear process for set-point tracking control. Simulation results demonstrate the effectiveness of the control strategy and the fast learning algorithm for multivariable non-linear processes. Comparison of the performance with PID control is included.

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Authors and Affiliations

  1. School of Engineering, Control Systems Research Group, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK

    D. W. Yu & D. L. Yu

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  1. D. W. Yu
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  2. D. L. Yu
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Correspondence to D. L. Yu.

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Yu, D.W., Yu, D.L. Neural network control of multivariable processes with a fast optimisation algorithm. Neural Comput & Applic 12, 185–189 (2003). https://doi.org/10.1007/s00521-003-0381-0

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  • DOI: https://doi.org/10.1007/s00521-003-0381-0

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