发表机构
Nanyang Technological University; VinUniversity（南洋理工大学; 越南大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对线性动力系统时变成本下的在线控制问题，提出利用短期预测的预测式在线控制算法，基于扰动-动作控制器参数化并引入全变差正则化，实现动态遗憾界，在电动激光雷达任务上提升定位精度与扫描完整性。
AI 中文摘要
本文研究了时变成本函数下线性动力系统的预测式在线控制问题，其动机源于电动激光雷达传感系统的实时自适应控制等应用场景。在这些应用中，控制器必须在线调整传感方向，以在变化的环境条件下平衡定位精度、扫描效率和平滑执行。我们提出了一种预测式在线控制（POC）算法，该算法利用对未来成本函数的短期预测，同时考虑系统动力学引起的记忆效应。通过使用扰动-动作控制器（DAC）参数化，在线控制问题被转化为关于策略参数的带记忆的在线凸优化（OCO）形式。我们开发了一种窗口化滚动时域更新方法，该方法融合了短期预测，并引入了全变差正则化项以抑制策略的剧烈变化。在理论上，我们证明了POC能够实现动态策略遗憾界，该界随比较器序列的路径长度增长，并给出了保证遗憾性能的关于预测和记忆窗口长度的对数充分条件。所提出的方法在电动激光雷达传感任务上进行了评估，展示了改进的定位精度以及传感精度与扫描完整性之间的良好权衡。
英文摘要
This paper studies predictive online control for linear dynamical systems with time-varying cost functions, motivated by applications such as real-time adaptive control of motorized LiDAR sensing systems, where the controller must adjust the sensing direction online to balance localization accuracy, scanning efficiency, and smooth actuation under changing environmental conditions. We propose a predictive online control (POC) algorithm that leverages short-term predictions of future cost functions while accounting for the memory effect induced by system dynamics. Using the disturbance-action controller (DAC) parameterization, the online control problem is transformed into an OCO-with-memory formulation over policy parameters. We develop a windowed receding-horizon update that incorporates short-term predictions and accommodates a total-variation regularizer to suppress abrupt policy variations. Theoretically, we prove that POC achieves a dynamic policy regret bound scaling with the path length of the comparator sequence, and provide sufficient conditions in terms of logarithmic prediction and memory horizons for the regret guarantee. The proposed method is evaluated on a motorized LiDAR sensing task, demonstrating improved localization accuracy together with a favorable trade-off between sensing accuracy and scanning completeness.
Comments16 pages
