Name
Abstract | ||
|---|---|---|
We propose an adaptive inverse control scheme, which employs a neural network for the system identification phase and updates its weights in online mode. The theoretical basis of the method is given and its performance is illustrated by means of its application to different control problems showing that our proposal is able to overcome the problems generated by dynamic nature of the process or by physical changes of the system which originate important modifications in the process. A comparative experimental study is presented in order to show the more stable behavior of the proposed method in several working ranks. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.15388/Informatica.2014.20 | Informatica, Lith. Acad. Sci. |
Keywords | Field | DocType |
adaptive inverse control,model predictive control,neural networks,neural predictor | Online learning,Computer science,Model predictive control,Inverse control,Artificial intelligence,Artificial neural network,System identification,Machine learning | Journal |
Volume | Issue | ISSN |
25 | 3 | 0868-4952 |
Citations | PageRank | References |
11 | 0.60 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| José Luís Calvo-Rolle | 1 | 175 | 41.67 |
| Oscar Fontenla-Romero | 2 | 337 | 39.49 |
| Beatriz Pérez-Sánchez | 3 | 95 | 14.03 |
| Bertha Guijarro-Berdiñas | 4 | 296 | 34.36 |