论文标题
部分可观测时空混沌系统的无模型预测
Data-Driven Model Predictive Control for Linear Time-Periodic Systems
论文作者
论文摘要
我们考虑了已知时期的未知离散时间线性时间周期（LTP）系统的数据驱动预测控制问题。我们提出的策略概括了具有数据支持的预测控制（DEEPC）和子空间预测控制（SPC），这些控制是建立的线性时间流动（LTI）系统的数据驱动控制技术。该方法得到了LTP系统行为系统理论的广泛理论发展，最终导致了基本引理的概括。我们的算法产生的结果与确定性LTP系统的标准模型预测控制（MPC）相同。通过模拟应用于随机多输入多输出LTP系统的算法的正规化版本，算法对噪声数据的鲁棒性进行了说明。
We consider the problem of data-driven predictive control for an unknown discrete-time linear time-periodic (LTP) system of known period. Our proposed strategy generalizes both Data-enabled Predictive Control (DeePC) and Subspace Predictive Control (SPC), which are established data-driven control techniques for linear time-invariant (LTI) systems. The approach is supported by an extensive theoretical development of behavioral systems theory for LTP systems, culminating in a generalization of the fundamental lemma. Our algorithm produces results identical to standard Model Predictive Control (MPC) for deterministic LTP systems. Robustness of the algorithm to noisy data is illustrated via simulation of a regularized version of the algorithm applied to a stochastic multi-input multi-output LTP system.
