Online SVM regression algorithm-based adaptive inverse control

Authors:
Hui Wang;Daoying Pi;Youxian Sun
Affiliations:
The National Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, PR China;The National Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, PR China;The National Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, PR China
Venue:
Neurocomputing
Year:
2007

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Abstract

An adaptive inverse control algorithm is proposed by combining fast online support vector machine regression (SVR) algorithm with straight inverse control algorithm. Because training speed of standard online SVR algorithm is very slow, a kernel cache-based method is developed to accelerate the standard algorithm and a new fast online SVR algorithm is obtained. Then the new algorithm is applied in straight inverse control for constructing the inverse model of controlled system online, and output errors of the system are used to control online SVR algorithm, which made the whole control system a closed-loop one. Simulation results show that the new algorithm has good control performance.