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Adaptive control for a class of nonlinear discrete-time systems using neural networks
- Source :
- Scopus-Elsevier
-
Abstract
- In this paper, the adaptive control problem is studied for a class of discrete-time unknown nonlinear systems with general relative degree in the presence of bounded disturbances. To derive the feedback control, a causal state-space model of the plant is obtained. By using an NN observer to estimate the unavailable but predictable states of the system, a Lyapunov-based adaptive state feedback NN controller is proposed. The state feedback control avoids the possible singularity problem in adaptive nonlinear control. The closed-loop system is proven to be semi-globally uniformly ultimately bounded (SGUUB). An arbitrarily small tracking error can be achieved if the size of neural networks is chosen large enough, and the control performance of the closed-loop system is guaranteed by suitably choosing the design parameters.
Details
- Database :
- OpenAIRE
- Journal :
- Scopus-Elsevier
- Accession number :
- edsair.doi.dedup.....13b0b7b225bcdb60763070d5114291ea