Barrier Lyapunov function-based tracking control for stochastic nonlinear systems with full-state constraints and input saturation
| Autor: | Na Duan, Huifang Min, Shengyuan Xu, Shumin Fei |
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| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Implicit function Computer Networks and Communications Computer science Applied Mathematics 02 engineering and technology Tracking (particle physics) Tracking error Nonlinear system 020901 industrial engineering & automation Control and Systems Engineering Control theory Backstepping Bounded function Signal Processing 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Saturation (chemistry) |
| Zdroj: | Journal of the Franklin Institute. 357:12414-12432 |
| ISSN: | 0016-0032 |
| DOI: | 10.1016/j.jfranklin.2020.09.022 |
| Popis: | This paper investigates the adaptive tracking control problem of stochastic nonlinear systems under the conditions of full-state constraints and input saturation. The barrier Lyapunov function (BLF) is applied to handle the full-state constraints. To deal with the input saturation, a distinctive method of introducing an auxiliary system is adopted. Then, a systematic controller design procedure is given by combining a novel radial basis function neural network (RBF NN) approximation approach with backstepping technique.By this way, an adaptive state-feedback controller with only one adaptive law is obtained, which renders the closed-loop system semi-globally uniformly ultimately bounded. Meanwhile, the tracking error is bounded by an explicit function of the design parameters and saturated input error. In addition, the full-states are not violated. Finally, a simple pendulum system and a numerical example are simulated to demonstrate the effectiveness of the proposed scheme. |
| Databáze: | OpenAIRE |
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