发表机构
University of California, Berkeley; University of California, Santa Barbara（加州大学伯克利分校; 加州大学圣塔芭芭拉分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种自适应采样控制框架，通过离线预计算不变集和在线实时计算MPC更新速率，在保证鲁棒约束满足的同时实现有限时间转换，并在巡航控制仿真中验证了其有效性。
AI 中文摘要
自适应采样控制在控制性能与资源效率之间取得平衡。然而，现有方法要么无法在速率转换期间保证鲁棒约束满足，要么需要计算成本高昂的在线优化。本文针对受多面体状态和输入约束以及有界加性扰动影响的线性系统，提出了一种自适应采样控制框架。给定由推理器提供的时变参考控制更新速率，我们的框架持续计算模型预测控制（MPC）更新速率，以确保在所有时间步上鲁棒地满足约束。离线时，利用鲁棒M步保持控制不变性，为一系列更新速率及它们之间的转换集预计算不变集。在线时，这些集合被实时用于保证递归可行性和对参考更新速率的有限时间转换。该架构的实用性在巡航控制仿真中得到了验证。
英文摘要
Adaptive-sampling control balances control performance with resource efficiency. However, existing methods either fail to guarantee robust constraint satisfaction during rate transitions or require computationally expensive online optimization. This paper proposes an adaptive-sampling control framework for linear systems subject to polytopic state and input constraints and bounded additive disturbances. Given a time-varying reference control update rate provided by a reasoner, our framework continuously calculates Model Predictive Control (MPC) update rates that ensure robust constraint satisfaction at all time steps. Offline, robust M-step hold control invariance is used to precompute invariant sets for a list of update rates and transition sets between them. Online, these sets are used in real time to guarantee recursive feasibility and finite-time transitions to the reference update rate. The utility of the architecture is demonstrated in a cruise control simulation.
