数据驱动预测控制的最大似然信号矩阵模型
Maximum Likelihood Signal Matrix Model for Data-Driven Predictive Control
摘要：
本文提出了一种数据驱动的预测控制框架的基础上直接从收集的数据的信号矩阵导出的隐式输入输出映射。这个信号矩阵模型是由最大似然估计与噪声污染的数据。通过在线线性化，隐式模型可以作为线性约束来描述滚动时域控制中系统可能的轨迹。信号矩阵也可以用新的测量值在线更新。该算法可以应用于大数据集和慢时变系统，可能具有高噪声水平。可以引入关于预测误差的附加正则化项以增强可预测性，从而增强控制性能。数值结果表明，所提出的信号矩阵模型预测控制算法在多种应用场合都是有效的，并且性能优于现有的数据驱动预测控制算法。
英文摘要：
The paper presents a data-driven predictive control framework based on an implicit input-output mapping derived directly from the signal matrix of collected data. This signal matrix model is derived by maximum likelihood estimation with noise-corrupted data. By linearizing online, the implicit model can be used as a linear constraint to characterize possible trajectories of the system in receding horizon control. The signal matrix can also be updated online with new measurements. This algorithm can be applied to large datasets and slowly time-varying systems, possibly with high noise levels. An additional regularization term on the prediction error can be introduced to enhance the predictability and thus the control performance. Numerical results demonstrate that the proposed signal matrix model predictive control algorithm is effective in multiple applications and performs better than existing data-driven predictive control algorithm.
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