Abstract
An adaptive control strategy for nonlinear systems is presented. The simple control law is derived based on minimizing a well-chosen performance index. Wavelets neural network model is applied to the scheme that can overcome the problem caused by the local minima when training the neural network. Compared with existing algorithms such as stochastic gradient algorithm, the present algorithm has the advantage of rapid convergence and low computational cost. The proposed approach is finally applied in a chemical reactor control problem. The simulation results proved that the proposed adaptive control method can effectively control unknown nonlinear systems.
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© 2006 Springer-Verlag Berlin Heidelberg
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Zhang, Z. (2006). Adaptive Controller Based on Wavelets Neural Network for a Class of Nonlinear Systems. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing. ICIC 2006. Lecture Notes in Computer Science, vol 4113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816157_32
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DOI: https://doi.org/10.1007/11816157_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37271-4
Online ISBN: 978-3-540-37273-8
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