Name
Title | ||
|---|---|---|
Training method for a sliding mode controller and quantified robustness against uncertainty |
Abstract | ||
|---|---|---|
Proposes a design method of a sliding mode controller by training a neural network. The singular solution of the optimal control problem is applied to training the neural network. We focus on the robustness of the trained controller against uncertainties, and we propose a method to quantify the robustness of any kind of controllers by training another neural network. Moreover, methods to train a quantified robust controller are proposed, and they can also improve the robustness. Some numerical simulations show the effectiveness of proposed methods |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1109/IJCNN.1999.832725 | IJCNN |
Keywords | Field | DocType |
control system synthesis,learning (artificial intelligence),neurocontrollers,nonlinear control systems,optimal control,robust control,uncertain systems,variable structure systems,quantified robust controller,singular solution,sliding mode controller,training method,sliding mode control,learning artificial intelligence,error correction,design method,control systems,switches,uncertainty,design methodology,neural network,neural networks,numerical simulation | Control theory,Optimal control,Control theory,Computer science,Singular solution,Robustness (computer science),Robust control,Artificial neural network,Uncertain systems | Conference |
Volume | ISSN | ISBN |
3 | 1098-7576 | 0-7803-5529-6 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nakanishi, H. | 1 | 3 | 4.15 |
| Koichi Inoue | 2 | 17 | 5.27 |