Abstract
A novel approach for the implementation of nonlinear model predictive control (MPC) is proposed using neural network and particle swarm optimization (PSO). A three-layered radial basis function neural network is used to generate multi-step predictive outputs of the controlled process. A modified PSO with simulated annealing is used at the optimization process in MPC. The proposed algorithm enhances the convergence and accuracy of the controller optimization. Applications to a discrete time nonlinear process and a thermal power unit load system are studied. The simulation results demonstrate the effectiveness of the proposed algorithm.
This work was supported by Key Science Project of Shanghai Education (04FA02).
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Wang, X., Xiao, J. (2005). PSO-Based Model Predictive Control for Nonlinear Processes. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_30
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DOI: https://doi.org/10.1007/11539117_30
Publisher Name: Springer, Berlin, Heidelberg
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