Adaptive neural control for an uncertain fractional‐order rotational mechanical system using disturbance observer
| Autor: | Shao-dong Chen, Shuyi Shao, Mou Chen, Qingxian Wu |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
0209 industrial biotechnology
Engineering Control and Optimization Adaptive control Computer simulation Artificial neural network business.industry Control engineering 02 engineering and technology Computer Science Applications Human-Computer Interaction Mechanical system 020901 industrial engineering & automation Order (biology) Control and Systems Engineering Control theory Disturbance observer 0202 electrical engineering electronic engineering information engineering Neural control 020201 artificial intelligence & image processing Electrical and Electronic Engineering Robust control business |
| Zdroj: | IET Control Theory & Applications. 10:1972-1980 |
| ISSN: | 1751-8652 |
| Popis: | In this study, a robust adaptive neural control is proposed for a fractional-order rotational mechanical system (FORMS) in the presence of system uncertainties and external unknown disturbances. System uncertainties of the FORMS are handled by the neural network (NN). To tackle unknown disturbances, a non-linear fractional-order disturbance observer (FODO) is explored for the FORMS. A robust adaptive control scheme is then developed by combining the NN with the designed FODO. Finally, numerical simulation results further demonstrate the effectiveness of the proposed tracking control scheme for the uncertain FORMS subject to external unknown disturbances. |
| Databáze: | OpenAIRE |
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