Abstract
In this paper we address the problem of augmenting a linear observer with an adaptive element. The design of the adaptive element employs two nonlinearly parameterized neural networks, the input and output layer weights of which are adapted on line. The goal is to improve the performance of the linear observer when applied to a nonlinear system. The networks teaching signal is generated using a second linear observer of the nominal system's error dynamics. Boundedness of signals is shown through Lyapunov's direct method. The approach is robust to unmodeled dynamics and disturbances. Simulations illustrate the theoretical results.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 4700-4705 |
| Number of pages | 6 |
| Journal | Proceedings of the IEEE Conference on Decision and Control |
| Volume | 4 |
| State | Published - Dec 1 2002 |
| Externally published | Yes |
| Event | 41st IEEE Conference on Decision and Control - Las Vegas, NV, United States Duration: Dec 10 2002 → Dec 13 2002 |
ASJC Scopus subject areas
- Control and Systems Engineering
- Modeling and Simulation
- Control and Optimization
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Cite this
Hovakimyan, N., Calise, A. J., & Madyastha, V. K. (2002). An adaptive observer design methodology for bounded nonlinear processes. Proceedings of the IEEE Conference on Decision and Control, 4, 4700-4705.