Abstract
Based on the Lyapunov synthesis approach, several adaptive neural control schemes have been developed during the last few years. So far, these schemes have been applied only to simple classes of nonlinear systems. This paper develops a design methodology that expands the class of nonlinear systems that adaptive neural control schemes can be applied to and relaxes some of the restrictive assumptions that are usually made. One such assumption is the requirement of a known bound on the network reconstruction error. The overall adaptive scheme is shown to guarantee semiglobal uniform ultimate boundedness. The proposed feedback control law is a smooth function of the state.
Keywords
Control theory (sociology)Adaptive controlNonlinear systemArtificial neural networkLyapunov functionAdaptive systemScheme (mathematics)Computer scienceMathematicsSimple (philosophy)Upper and lower boundsControl (management)Artificial intelligence
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Publication Info
- Year
- 1996
- Type
- article
- Volume
- 41
- Issue
- 3
- Pages
- 447-451
- Citations
- 1452
- Access
- Closed
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Cite This
Marios M. Polycarpou
(1996).
Stable adaptive neural control scheme for nonlinear systems.
IEEE Transactions on Automatic Control
, 41
(3)
, 447-451.
https://doi.org/10.1109/9.486648
Identifiers
- DOI
- 10.1109/9.486648