B-spline neural networks based PID controller for Hammerstein systemsHong, 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. Full text not archived in this repository. It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.1007/978-3-642-31837-5_6 Abstract/SummaryA 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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