Abstract
A new predictive control scheme for nonlienar system is propoesed in this paper. In order to generate a set of optimization variables which have the same number of chaotic variables first, and at the same time to enlarge the scope of chaotic motion to the range of optimization variables, a new mixed particle swarm optimization (MPSO) algorithm is constructed. Then, this method is used to train the parameters of RBF neural network (NN). This NN can identify nonliear system with an acceptable accuracy, which can be seen from the simulation example. Furthermore, a direct multi-step predictive control scheme based on the MPSO-RBF neural network is proposed for nonlinear system. Simulation results manifest that the proposed method is effective and efficient.
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Zhang, Y., Zhang, L., Xing, G., Yang, P. (2011). Predictive Control of Nonlinear System Based on MPSO-RBF Neural Network. In: Qi, L. (eds) Information and Automation. ISIA 2010. Communications in Computer and Information Science, vol 86. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19853-3_84
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DOI: https://doi.org/10.1007/978-3-642-19853-3_84
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19852-6
Online ISBN: 978-3-642-19853-3
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