Abstract
A new designing method of a robust neural network controller against system environment changes using Universal Learning Network (ULN) is considered in this paper. With the introduced method, the worst values of system parameters can be searched as well as the optimization of controller parameters through a dual learning algorithm, which includes maximization and minimization procedures. Therefore, the robust controller can be obtained by minimizing the criterion function regarding the worst values of system parameters. Simulation results demonstrate that the system performance has been improved compared with the conventional method by using the proposed method.
| Original language | English |
|---|---|
| Pages | 497-502 |
| Number of pages | 6 |
| Publication status | Published - 2001 Jan 1 |
| Event | International Joint Conference on Neural Networks (IJCNN'01) - Washington, DC, United States Duration: 2001 Jul 15 → 2001 Jul 19 |
Conference
| Conference | International Joint Conference on Neural Networks (IJCNN'01) |
|---|---|
| Country | United States |
| City | Washington, DC |
| Period | 01/7/15 → 01/7/19 |
ASJC Scopus subject areas
- Software
- Artificial Intelligence
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Cite this
Chen, H., Hirasawa, K., Hu, J., & Murata, J. (2001). A new robust neural network controller designing method for nonlinear systems. 497-502. Paper presented at International Joint Conference on Neural Networks (IJCNN'01), Washington, DC, United States.