Support vector networks in adaptive friction compensation.
This paper presents our research on how support vector regression (SVR) and parametric adaptive learning, which are normally used independently, can be exploited together to benefit adaptive neural control. In the context of friction compensation for servo-motion control systems, we present the notion of support vector networks which play an essential role in combining SVR and adaptive neural network (NN) in cooperation for friction estimation. The analysis shows that the proposed support vector network contributes not only to the performance improvement but also to the practical usefulness in adaptive friction compensation. Experimental results are reported to demonstrate the effectiveness of the proposed approach.
School of Information Science and Technology, Sun Yat-Sen University, Guangzhou 510275, P.R. China.
G L
GL
Wang
Y F
YF
Li
D X
DX
Bi
D000465:Algorithms; D003198:Computer Simulation; D003661:Decision Support Techniques; D005246:Feedback; D017276:Friction; D019563:Mechanics; D008962:Models, Theoretical; D016571:Neural Networks, Computer