Fast online SVR algorithm based adaptive internal model control

Authors:
Hui Wang;Daoying Pi;Youxian Sun;Chi Xu;Sizhen Chu
Affiliations:
The National Laboratory of Industrial Control Technology, Institute of Modern Control Engineering, Zhejiang University, Hangzhou, P.R. China;The National Laboratory of Industrial Control Technology, Institute of Modern Control Engineering, Zhejiang University, Hangzhou, P.R. China;The National Laboratory of Industrial Control Technology, Institute of Modern Control Engineering, Zhejiang University, Hangzhou, P.R. China;Hangzhou Automation Technology Institute, Hangzhou, China;Hangzhou Automation Technology Institute, Hangzhou, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Year:
2006

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Abstract

Based on fast online support vector regression (SVR) algorithm, reverse model of system model is constructed, and adaptive internal model controller is developed. First, SVR model and its online training algorithm are introduced. A kernel cache method is used to accelerate the online training algorithm, which makes it suitable for real-time control application. Then it is used in internal model control (IMC) for online constructing internal model and designing the internal model controller. Output errors of the system are used to control online SVR algorithm, which made the whole control system a closed-loop one. Last, the fast online SVM based adaptive internal model control was used to control a benchmark nonlinear system. Simulation results show that the controller has simple structure, good control performance and robustness.