Disturbance-Improved Model-Free Adaptive Prediction Control for Discrete-Time Nonlinear Systems with Time Delay
This study proposes a Disturbance-improved Model-free Adaptive Prediction Control (DMFAPC) algorithm for a discrete-time nonlinear system with time delay and disturbance. The algorithm is shown to have good robustness. On the one hand, the Smith predictor is used to predict the output at a future ti...
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| Autores principales: | , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
MDPI AG
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/81e27956440b492a9064564f20baeebd |
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| Sumario: | This study proposes a Disturbance-improved Model-free Adaptive Prediction Control (DMFAPC) algorithm for a discrete-time nonlinear system with time delay and disturbance. The algorithm is shown to have good robustness. On the one hand, the Smith predictor is used to predict the output at a future time to eliminate the time delay in the system; on the other hand, an attenuation factor is introduced at the input to effectively eliminate the measurement disturbance. The proposed algorithm is a data-driven control algorithm that does not require the model information of the controlled system; it only requires the input and output data. The convergence of the DMFAPC is analyzed. Simulation results confirm the effectiveness of this algorithm. |
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