Abstract
A decentralized adaptive output feedback control design is proposed for large-scale interconnected systems. It is assumed that all the controllers share prior information about the system reference models. Based on that information, a linearly parameterized neural network is introduced for each subsystem to partially cancel the effect of the interconnections on the tracking performance. Boundedness of error signals is shown through Lyapunov's direct method.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1699-1704 |
| Number of pages | 6 |
| Journal | Proceedings of the IEEE Conference on Decision and Control |
| Volume | 2 |
| State | Published - Dec 1 2003 |
| Externally published | Yes |
| Event | 42nd IEEE Conference on Decision and Control - Maui, HI, United States Duration: Dec 9 2003 → Dec 12 2003 |
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ASJC Scopus subject areas
- Control and Systems Engineering
- Modeling and Simulation
- Control and Optimization
Cite this
Decentralized Adaptive Output Feedback Control via Input/Output Inversion. / Hovakimyan, Naira; Lavretsky, Eugene; Calise, Anthony J.; Sattigeri, Ramachandra.
In: Proceedings of the IEEE Conference on Decision and Control, Vol. 2, 01.12.2003, p. 1699-1704.Research output: Contribution to journal › Conference article
}
TY - JOUR
T1 - Decentralized Adaptive Output Feedback Control via Input/Output Inversion
AU - Hovakimyan, Naira
AU - Lavretsky, Eugene
AU - Calise, Anthony J.
AU - Sattigeri, Ramachandra
PY - 2003/12/1
Y1 - 2003/12/1
N2 - A decentralized adaptive output feedback control design is proposed for large-scale interconnected systems. It is assumed that all the controllers share prior information about the system reference models. Based on that information, a linearly parameterized neural network is introduced for each subsystem to partially cancel the effect of the interconnections on the tracking performance. Boundedness of error signals is shown through Lyapunov's direct method.
AB - A decentralized adaptive output feedback control design is proposed for large-scale interconnected systems. It is assumed that all the controllers share prior information about the system reference models. Based on that information, a linearly parameterized neural network is introduced for each subsystem to partially cancel the effect of the interconnections on the tracking performance. Boundedness of error signals is shown through Lyapunov's direct method.
UR - http://www.scopus.com/inward/record.url?scp=1542290171&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=1542290171&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:1542290171
VL - 2
SP - 1699
EP - 1704
JO - Proceedings of the IEEE Conference on Decision and Control
JF - Proceedings of the IEEE Conference on Decision and Control
SN - 0191-2216
ER -