Abstract
This paper presents an adaptive control scheme for nonlinear black-box systems based on the use of Universal Learning Networks (ULN). A ULN nonlinear controller is constructed in a similar way to linear stochastic control theory. In the obtained ULN controller, some node functions are known, while others are unknown. Each unknown node function is re-parameterized using an adaptive fuzzy model. A robust adaptive algorithm is developed to adjust the unknown parameters in the controller. The effectiveness of the proposed control scheme is examined via numerical simulations.
| Original language | English |
|---|---|
| Pages | 2453-2458 |
| Number of pages | 6 |
| Publication status | Published - 1998 Jan 1 |
| Event | Proceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3) - Anchorage, AK, USA Duration: 1998 May 4 → 1998 May 9 |
Other
| Other | Proceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3) |
|---|---|
| City | Anchorage, AK, USA |
| Period | 98/5/4 → 98/5/9 |
ASJC Scopus subject areas
- Software
Fingerprint Dive into the research topics of 'Adaptive control of nonlinear black-box systems based on Universal Learning Networks'. Together they form a unique fingerprint.
Cite this
Hu, J., Hirasawa, K., Murata, J., Ohbayashi, M., & Kumamaru, K. (1998). Adaptive control of nonlinear black-box systems based on Universal Learning Networks. 2453-2458. Paper presented at Proceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3), Anchorage, AK, USA, .