发表机构
Robotics Research Center, International Institute of Information Technology - Hyderabad（海得拉巴国际信息技术学院机器人研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对未知非线性MIMO系统的时间到达-避开-停留任务，提出执行器感知时空管合成框架。该框架将执行器约束纳入管合成过程，利用伯恩斯坦多项式基函数和推导的约束进行优化，仿真显示能遵守执行器极限并减少控制努力。
AI 中文摘要
本文提出了一种执行器感知的时空管（STT）合成框架，用于在执行器约束下为未知非线性多输入多输出（MIMO）系统完成时间到达-避开-停留（T-RAS）任务。现有STT合成方法在管生成后通过重复在线重新优化或控制器重新设计来处理执行器饱和问题。本文框架则将执行器约束直接纳入管合成过程。利用伯恩斯坦多项式基函数对STT中心线和宽度进行参数化，通过分析用于STT跟踪的无近似规定性能控制器（PPC）的最坏情况闭环误差动态，推导出线性执行器可行性约束。将约束直接根据管的伯恩斯坦控制点嵌入到STT合成优化中，以生成执行器可行的管，无需在线重新优化或控制器重新设计。对执行T-RAS任务的全向移动机器人的仿真研究表明，该框架在整个任务中遵守规定的执行器极限，与现有STT合成方法相比，所需控制努力减少了约50%。
英文摘要
This work proposes an actuator-aware spatiotemporal tube (STT) synthesis framework to accomplish temporal reach-avoid-stay (T-RAS) tasks for an unknown nonlinear multi-input and multi-output (MIMO) system under actuator constraints. Existing STT synthesis methods address actuator saturation after the tube generation either through repeated online re-optimization or controller redesign. Instead, the proposed framework incorporates actuator constraints directly into the tube synthesis process. The STT centerline and width are parameterized using Bernstein polynomial basis functions, whose convex-hull property enables sample-free enforcement of geometric and derivative constraints. By analyzing the worst-case closed-loop error dynamics of an approximation-free prescribed performance controller (PPC) used for STT tracking, we derive a linear actuator feasibility constraint. The constraints are embedded directly in terms of the tubes' Bernstein control points into the STT synthesis optimization for actuator-feasible tube generation, eliminating the need for online re-optimization or controller redesign. A simulation study on an omnidirectional mobile robot performing a T-RAS task shows that the proposed framework adheres to the prescribed actuator limits throughout the task and reduces required control effort by approximately $50\%$ compared with an existing STT synthesis method.
