论文标题
使用可及性分析对未知非线性系统对数据驱动的强大预测控制
Robust Data-Driven Predictive Control of Unknown Nonlinear Systems using Reachability Analysis
论文作者
论文摘要
这项工作提出了在存在有限的过程和测量噪声的情况下，针对未知非线性系统的数据驱动的预测控制方法。用于控制器设计的数据驱动的可触及设置，而不是使用明确的非线性系统模型。尽管过程和测量噪声是界限的，但噪声的统计特性并不需要已知。通过在学习阶段使用过去的嘈杂输入输出数据，我们提出了一种新颖的方法，以过度可容纳未知的非线性系统。然后，我们提出了一种数据驱动的预测控制方法，以从嘈杂的在线数据中计算安全，健壮的控制策略。通过有效利用在学习阶段获得的预测输出集，可以在控制阶段保证限制因素，并具有稳健的安全边缘。最后，一个数值示例验证了所提出的方法的功效，并通过基于模型的预测控制方法证明了可比性的性能。
This work proposes a robust data-driven predictive control approach for unknown nonlinear systems in the presence of bounded process and measurement noise. Data-driven reachable sets are employed for the controller design instead of using an explicit nonlinear system model. Although the process and measurement noise are bounded, the statistical properties of the noise are not required to be known. By using the past noisy input-output data in the learning phase, we propose a novel method to over-approximate reachable sets of an unknown nonlinear system. Then, we propose a data-driven predictive control approach to compute safe and robust control policies from noisy online data. The constraints are guaranteed in the control phase with robust safety margins through the effective use of the predicted output reachable set obtained in the learning phase. Finally, a numerical example validates the efficacy of the proposed approach and demonstrates comparable performance with a model-based predictive control approach.
