AI 中文总结
本文针对线性参数变时系统，提出一种子空间数据驱动预测控制方法，通过降阶预测器等设计，提升了控制器的鲁棒性与计算效率，实现了可实际部署的多步控制方案。
AI 中文摘要
本文提出一种针对线性参数变时（LPV）系统的子空间数据驱动预测控制方法。从创新形式的仿射LPV状态空间模型出发，推导得到一个多步预测器，该预测器可分离过去数据、未来输入、调度轨迹与创新项的影响。通过将该表示投影到提升后的输入-输出-调度数据的行空间，得到一个渐近无偏的数据驱动预测器，该预测器可直接嵌入后退时域控制问题，无需显式辨识LPV模型。为使所得LPV数据驱动预测控制（DDPC）公式可处理，我们基于LQ分解引入γ-DDPC的LPV扩展形式，该公式固定在线决策变量数量，与数据集长度无关。随后提出一种降阶预测器以抑制依赖调度的回归量的指数增长，同时放宽持续激励条件。仿真研究（包括不平衡圆盘示例）表明，所提控制器具有良好的跟踪性能，与现有LPV DDPC方案相比，对测量噪声的鲁棒性更优且计算成本更低，使多步LPV DDPC在更长的过去时域下也可实际部署。
英文摘要
This paper presents a subspace data-driven predictive control method for linear parameter-varying (LPV) systems. Starting from an affine LPV state-space model in innovation form, we derive a multi-step predictor that separates the effects of past data, future inputs, scheduling trajectories, and innovations. By projecting this representation onto the row span of lifted input-output-scheduling data, we obtain an asymptotically unbiased data-driven predictor that can be embedded directly in a receding-horizon control problem, without explicitly identifying an LPV model. To make the resulting LPV data-driven predictive control (DDPC) formulation tractable, we introduce an LPV extension of $γ$-DDPC based on an LQ factorization. This formulation fixes the number of online decision variables independently of the length of the dataset. A reduced-order predictor is then proposed to curb the exponential growth of scheduling-dependent regressors, which also relaxes the persistence-of-excitation condition. Simulation studies, including an unbalanced-disk example, show that the proposed controller achieves good tracking performance and, compared to existing LPV DDPC schemes, achieves better robustness to measurement noise and reduced computational cost, making multi-step LPV DDPC practically deployable, even with longer past horizons.
Comments16 pages. Submitted to Automatica
