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 |
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Pages | 2453-2458 |
Number of pages | 6 |
Publication status | Published - 1998 Jan 1 |
Externally published | Yes |
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) |
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City | Anchorage, AK, USA |
Period | 98/5/4 → 98/5/9 |
ASJC Scopus subject areas
- Software