论文标题
基于管子的数据驱动预测控制
Tube-Based Zonotopic Data-Driven Predictive Control
论文作者
论文摘要
我们提出了一种基于有界成瘾性干扰影响的线性系统的新型基于数据驱动的预测控制方法。我们的方法利用了未知线性系统的可及性分析的最新结果来制定和解决基于强大的管子的预测控制问题。更确切地说，我们的方法在于从收集的数据中得出包含真实状态错误集的划界。我们展示了如何确保所得错误划界的稳定性，可以利用这些误差界限，以提高现有数据型数据驱动的MPC公式的计算效率。受强对抗噪声影响的双整合器的结果证明了拟议的控制方法的有效性。
We present a novel tube-based data-driven predictive control method for linear systems affected by a bounded addictive disturbance. Our method leverages recent results in the reachability analysis of unknown linear systems to formulate and solve a robust tube-based predictive control problem. More precisely, our approach consists in deriving, from the collected data, a zonotope that includes the true state error set. We show how to guarantee the stability of the resulting error zonotope, which can be exploited to increase the computational efficiency of existing zonotopic data-driven MPC formulations. Results on a double-integrator affected by strong adversarial noise demonstrate the effectiveness of the proposed control approach.
