SVM Based Nonlinear Self-tuning Control
In this paper, a support vector machine (SVM) with polynomial kernel function enhanced nonlinear self-tuning controller is developed, which combines the SVM identifier and parameters’ modifier together. The inverse model of a nonlinear system is achieved by off-line black-box identification according to input and output data. Then parameters of the model are modified online using gradient descent algorithm. Simulation results show that SVM based self-tuning control can be well applied to nonlinear uncertain system. And the SVM based self-tuning control of nonlinear system has good robustness performance in tracking reference input with good generalization ability.
KeywordsSupport Vector Machine Inverse Model Reference Input Gradient Descent Algorithm Nonlinear Uncertain System
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- 1.Xu, L.N.: Neural Network Control. Harbin Institute of Technology Press, Harbin (1999)Google Scholar
- 2.Han, Z.J.: Adaptive Control. Tsinghua University Press, Beijing (2000)Google Scholar
- 5.Zhong, W.M., Pi, D.Y., Sun, Y.X.: SVM with Quadratic Polynomial Kernel Function based Nonlinear Model One-step-ahead Predictive Control. Chinese Journal of Chemical Engineering 13(3), 373–379 (2005)Google Scholar