Neural Adaptive Self-Triggered Control for Uncertain Nonlinear Systems With Input Hysteresis
| Autor: | Kemao Ma, Hongkang Zhang, Zhi Liu, Jianhui Wang, C. L. Philip Chen |
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| Rok vydání: | 2022 |
| Předmět: |
Implicit function
Computer Networks and Communications Computer science Uncertainty Tracking (particle physics) Computer Science Applications Tracking error Nonlinear system Nonlinear Dynamics Artificial Intelligence Control theory Backstepping Bounded function Computer Simulation Neural Networks Computer Realization (systems) Software |
| Zdroj: | IEEE Transactions on Neural Networks and Learning Systems. 33:6206-6214 |
| ISSN: | 2162-2388 2162-237X |
| Popis: | The issue of neural adaptive self-triggered tracking control for uncertain nonlinear systems with input hysteresis is considered. Combining radial basis function neural networks (RBFNNs) and adaptive backstepping technique, an adaptive self-triggered tracking control approach is developed, where the next trigger instant is determined by the current information. Compared with the event-triggered control mechanism, its biggest advantage is that it does not need to continuously monitor the trigger condition of the system, which is convenient for physical realization. By the proposed controller, the hysteresis's effect can be compensated effectively and the tracking error can be bounded by an explicit function of design parameters. Simultaneously, all other signals in the closed-loop system can be remaining bounded. Finally, two examples are presented to verify the effectiveness of the proposed method. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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