Name
Abstract | ||
|---|---|---|
In this paper, a support vector machine (SVM) with linear kernel function based nonparametric model identification and dynamic matrix control (SVM_DMC) technique is presented. First, a step response model involving manipulated variables is obtained via system identification by SVM with linear kernel function according to random test data or manufacturing data. Second, an explicit control law of a receding horizon quadric objective is gotten through the predictive control mechanism. Final, the approach is illustrated by a simulation of a system with dead time delay. The results show that SVM_DMC technique has good performance in predictive control with good capability in keeping reference trajectory. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11539087_93 | ICNC (1) |
Keywords | Field | DocType |
svm_dmc technique,dynamic model control,linear kernel function,dynamic matrix control,predictive control,good capability,good performance,predictive control mechanism,manufacturing data,explicit control law,linear kernel,nonparametric model identification,support vector machine,kernel function,system identification,random testing,model identification | Step response,Mathematical optimization,Control theory,Computer science,Support vector machine,Model predictive control,Algorithm,Test data,System identification,Quadric,Random function,Kernel (statistics) | Conference |
Volume | ISSN | ISBN |
3610 | 0302-9743 | 3-540-28323-4 |
Citations | PageRank | References |
1 | 0.39 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weimin Zhong | 1 | 79 | 14.18 |
| Daoying Pi | 2 | 50 | 9.21 |
| Youxian Sun | 3 | 2707 | 196.15 |