Abstract
A new PID tuning and controller approach is introduced for Hammerstein systems based on input/output data. A B-spline neural network is used to model the nonlinear static function in the Hammerstein system. The control signal is composed of a PID controller together with a correction term. In order to update the control signal, the multistep ahead predictions of the Hammerstein system based on the B-spline neural networks and the associated Jacobians matrix are calculated using the De Boor algorithms including both the functional and derivative recursions. A numerical example is utilized to demonstrate the efficacy of the proposed approaches.
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Hong, X., Iplikci, S., Chen, S., Warwick, K. (2012). B-Spline Neural Networks Based PID Controller for Hammerstein Systems. In: Huang, DS., Gupta, P., Zhang, X., Premaratne, P. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2012. Communications in Computer and Information Science, vol 304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31837-5_6
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DOI: https://doi.org/10.1007/978-3-642-31837-5_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31836-8
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