方法对比
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| 主动抗扰控制× | 模型预测控制× | |
|---|---|---|
| 领域 | 控制理论 | 控制理论 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2009 | 1978 |
| 提出者≠ | Jingquan Han | Jacques Richalet |
| 类型 | algorithm | algorithm |
| 开创性文献≠ | Han, J. (2009). From PID to active disturbance rejection control. IEEE Transactions on Industrial Electronics, 56(3), 900-906. DOI ↗ | Richalet, J., Rault, A., Testud, J., & Papon, J. (1978). Model predictive heuristic control. Automatica, 14(5), 413-428. DOI ↗ |
| 别名 | ADRC, Disturbance Rejection Control | MPC, Receding Horizon Control |
| 相关≠ | 2 | 5 |
| 摘要≠ | Active Disturbance Rejection Control (ADRC) is a control method that estimates and cancels disturbances and model uncertainties in real-time using an extended state observer (ESO), treating them as additional 'disturbance states'. Developed by Han and popularized by Gao, ADRC achieves remarkable robustness without requiring precise plant models, making it practical for real-world systems with significant uncertainty and disturbances. | Model Predictive Control (MPC) is an advanced control strategy that uses an explicit process model to predict future system behavior over a finite horizon and solves an optimization problem at each control step. First formalized by Richalet et al. in 1978, MPC has become the dominant approach in process control industries, from chemical plants to autonomous vehicles, because it naturally handles constraints and can optimize multiple objectives simultaneously. |
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