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
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.