Abstract
A direct adaptive output feedback control design procedure is developed for highly uncertain nonlinear systems, that does not rely on state estimation. The approach is also applicable to systems of unknown, but bounded dimension. This includes systems with both parametric uncertainties and unmodelled dynamics. This result is achieved by extending the universal function approximation property of linearly parameterized neural networks to model unknown system dynamics from input/output data. The network weight adaptation rule is derived from Lyapunov stability analysis, that guarantees boundedness of the NN weights and the system tracking errors. Numerical simulations of an output feedback controlled Van der Pol oscillator, coupled with a linear oscillator, are used to illustrate the practical potential of the theoretical results.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 3134-3139 |
| Number of pages | 6 |
| Journal | Proceedings of the IEEE Conference on Decision and Control |
| Volume | 4 |
| State | Published - Dec 1 2001 |
| Externally published | Yes |
| Event | 40th IEEE Conference on Decision and Control (CDC) - Orlando, FL, United States Duration: Dec 4 2001 → Dec 7 2001 |
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ASJC Scopus subject areas
- Control and Systems Engineering
- Modeling and Simulation
- Control and Optimization
Cite this
An SPR approach for adaptive output feedback control with neural networks. / Calise, Anthony J.; Hovakimyan, Naira; Idan, Moshe.
In: Proceedings of the IEEE Conference on Decision and Control, Vol. 4, 01.12.2001, p. 3134-3139.Research output: Contribution to journal › Conference article
}
TY - JOUR
T1 - An SPR approach for adaptive output feedback control with neural networks
AU - Calise, Anthony J.
AU - Hovakimyan, Naira
AU - Idan, Moshe
PY - 2001/12/1
Y1 - 2001/12/1
N2 - A direct adaptive output feedback control design procedure is developed for highly uncertain nonlinear systems, that does not rely on state estimation. The approach is also applicable to systems of unknown, but bounded dimension. This includes systems with both parametric uncertainties and unmodelled dynamics. This result is achieved by extending the universal function approximation property of linearly parameterized neural networks to model unknown system dynamics from input/output data. The network weight adaptation rule is derived from Lyapunov stability analysis, that guarantees boundedness of the NN weights and the system tracking errors. Numerical simulations of an output feedback controlled Van der Pol oscillator, coupled with a linear oscillator, are used to illustrate the practical potential of the theoretical results.
AB - A direct adaptive output feedback control design procedure is developed for highly uncertain nonlinear systems, that does not rely on state estimation. The approach is also applicable to systems of unknown, but bounded dimension. This includes systems with both parametric uncertainties and unmodelled dynamics. This result is achieved by extending the universal function approximation property of linearly parameterized neural networks to model unknown system dynamics from input/output data. The network weight adaptation rule is derived from Lyapunov stability analysis, that guarantees boundedness of the NN weights and the system tracking errors. Numerical simulations of an output feedback controlled Van der Pol oscillator, coupled with a linear oscillator, are used to illustrate the practical potential of the theoretical results.
UR - http://www.scopus.com/inward/record.url?scp=0035712592&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0035712592&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:0035712592
VL - 4
SP - 3134
EP - 3139
JO - Proceedings of the IEEE Conference on Decision and Control
JF - Proceedings of the IEEE Conference on Decision and Control
SN - 0191-2216
ER -