Adaptive recursive sliding-mode dynamic surface and its event-triggered control of uncertain non-affine systems
| Autor: | Tengfei Zhang, Yang Yang, Qing Meng, Dong Yue, Jiaben Liang |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Lyapunov function
0209 industrial biotechnology Artificial neural network Computer Networks and Communications Computer science Applied Mathematics 02 engineering and technology Tracking error symbols.namesake Nonlinear system 020901 industrial engineering & automation Control and Systems Engineering Control theory Bounded function Signal Processing 0202 electrical engineering electronic engineering information engineering symbols 020201 artificial intelligence & image processing Radial basis function Affine transformation Actuator |
| Zdroj: | Journal of the Franklin Institute. 357:3469-3497 |
| ISSN: | 0016-0032 |
| DOI: | 10.1016/j.jfranklin.2020.02.034 |
| Popis: | An adaptive neural network control strategy and its event-triggered controller are designed for non-affine pure-feedback uncertain systems based on nonlinear gains and recursive sliding-mode dynamic surfaces. A kind of nonlinear gain functions is introduced into the traditional framework of dynamic surface control (DSC) with recursive sliding-mode to make a compromise between control accuracy and transient performance. Radial basis function (RBF) neural networks (NNs) are adopted to approximate unknown functions at each step, and novel adaptive update laws with leakage terms of σ-modification is constructed. To reduce the action number of the actuator, an extended control strategy in an event-triggered manner is proposed. By the Lyapunov function, it is proven that both of the two control strategies can force the tracking error arbitrarily small and guarantee all the signals in the closed-loop system uniformly ultimately bounded. Finally, simulation results are provided to verify the effectiveness of the proposed control strategy. |
| Databáze: | OpenAIRE |
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