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B-spline neural networks based PID controller for Hammerstein systems

Hong, X., Iplikci, S., Chen, S. and Warwick, K. (2012) B-spline neural networks based PID controller for Hammerstein systems. In: Huang, D.-S., Gupta, P., Zhang, X. and Premaratne, P. (eds.) Emerging Intelligent Computing Technology and Applications. Communications in Computer and Information Science, 304. Springer, pp. 38-46.

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To link to this article DOI: 10.1007/978-3-642-31837-5_6

Abstract/Summary

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.

Item Type:Book or Report Section
Refereed:Yes
Divisions:Faculty of Science > School of Systems Engineering
ID Code:28184
Additional Information:Proceedings 8th International Conference, ICIC 2012, Huangshan, China, July 25-29, 2012.
Publisher:Springer
Publisher Statement:

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