Name
Abstract | ||
|---|---|---|
This paper proposes an improved scheme for feedback error learning (FEL). In two-degree-of-freedom control systems in general, a prefilter is used to compensate the relative degree delay of a strictly proper plant. In conventional schemes of FEL, however, the feedforward controller has to learn parameter including the prefilter, although it is given in advance. The proposed scheme reduces this redundancy by means of the prefilter state variables as part of the feedforward signals. Furthermore, the learning law by Muramatsu et al. is generalized to the MIMO case under a recursive least square criterion. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/CCA.2010.5611303 | Control Applications |
Keywords | Field | DocType |
MIMO systems,error analysis,feedback,feedforward,filtering theory,learning systems,least squares approximations,recursive estimation,MIMO,feedback error learning,feedforward signals,prefilter state variable,recursive least square criterion,relative degree delay,two-degree-of-freedom control systems | Feedforward neural network,Control theory,Polynomial,Control theory,Computer science,MIMO,Control engineering,Redundancy (engineering),State variable,Control system,Feed forward | Conference |
ISSN | ISBN | Citations |
1085-1992 | 978-1-4244-5363-4 | 2 |
PageRank | References | Authors |
0.48 | 2 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kenji Sugimoto | 1 | 30 | 10.35 |
| Makoto Noguchi | 2 | 2 | 0.48 |