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 |
|---|---|
| Title of host publication | IEEE International Conference on Neural Networks - Conference Proceedings |
| Editors | Anon |
| Place of Publication | Piscataway, NJ, United States |
| Publisher | IEEE |
| Pages | 2453-2458 |
| Number of pages | 6 |
| Volume | 3 |
| Publication status | Published - 1998 |
| 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) |
|---|---|
| City | Anchorage, AK, USA |
| Period | 98/5/4 → 98/5/9 |
Fingerprint
ASJC Scopus subject areas
- Software
Cite this
Adaptive control of nonlinear black-box systems based on Universal Learning Networks. / Furuzuki, Takayuki; Hirasawa, Kotaro; Murata, Junichi; Ohbayashi, Masanao; Kumamaru, Kousuke.
IEEE International Conference on Neural Networks - Conference Proceedings. ed. / Anon. Vol. 3 Piscataway, NJ, United States : IEEE, 1998. p. 2453-2458.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Adaptive control of nonlinear black-box systems based on Universal Learning Networks
AU - Furuzuki, Takayuki
AU - Hirasawa, Kotaro
AU - Murata, Junichi
AU - Ohbayashi, Masanao
AU - Kumamaru, Kousuke
PY - 1998
Y1 - 1998
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=0031642355&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0031642355&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:0031642355
VL - 3
SP - 2453
EP - 2458
BT - IEEE International Conference on Neural Networks - Conference Proceedings
A2 - Anon, null
PB - IEEE
CY - Piscataway, NJ, United States
ER -