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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proc. IEEE International Conference on Neural Networks, Perth, Australia, Piscataway, pp. 1942–1948 (1995)
Asanga, R., Halgamuge, S.K., Watson, H.C.: Self-Organizing Hirrarchical Particle Swaem Optimizer with Time-Varying Acceleration Coefficients. IEEE Transaction on Evolutionary Computation 3(8), 240–255 (2000)
Li, B., Jiang, W.: Chaos optimization method and its application. Control Theory and Applications 18(4), 613–615 (1997)
Sheng, Z., Yin, Q.: Equipment condition monitoring and fault diagnosis technology and its application. Chemical Industry Press, Beijing (2003)
Zhang, S., Li, K., Zhang, S., et al.: A design program based on RBF nonlinear system’s inverse control. System Simulation Technology 18(9), 2688–2690 (2006)
Zhang, R., Wang, S.: Multi-step predictive control based on neural network’s nonlinear systems. Control and Decision 20(3), 332–336 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
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
eBook Packages: Computer ScienceComputer Science (R0)