Adaptive neural dynamic surface control of strict-feedback nonlinear systems with full state constraints and unmodeled dynamics
| Autor: | Meizhen Xia, Tianping Zhang, Yang Yi |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Lyapunov function
Surface (mathematics) 0209 industrial biotechnology Adaptive control Artificial neural network 02 engineering and technology State (functional analysis) Signal Nonlinear system symbols.namesake 020901 industrial engineering & automation Control and Systems Engineering Control theory 0202 electrical engineering electronic engineering information engineering symbols 020201 artificial intelligence & image processing Radial basis function Electrical and Electronic Engineering Mathematics |
| Zdroj: | Automatica. 81:232-239 |
| ISSN: | 0005-1098 |
| DOI: | 10.1016/j.automatica.2017.03.033 |
| Popis: | In this paper, the problem of adaptive neural network (NN) dynamic surface control (DSC) is discussed for a class of strict-feedback nonlinear systems with full state constraints and unmodeled dynamics. By introducing a one to one nonlinear mapping, the strict-feedback system with full state constraints is transformed into a novel pure-feedback system without state constraints. Radial basis function (RBF) neural networks (NNs) are used to approximate unknown nonlinear continuous functions. Unmodeled dynamics is dealt with by introducing a dynamical signal. Using modified DSC and introducing integral-type Lyapunov function, adaptive NN DSC is developed. Using Young’s inequality, only one parameter is adjusted at each recursive step in the design. It is shown that all the signals in the closed-loop system are semi-global uniform ultimate boundedness (SGUUB), and the full state constraints are not violated. Simulation results are provided to verify the effectiveness of the proposed approach. |
| Databáze: | OpenAIRE |
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