从连续设计到时延感知的离散综合：永磁同步电机（PMSM）驱动的保证高带宽联合控制
From Continuous Design to Delay-Aware Discrete Synthesis: Guaranteed High-Bandwidth Joint Control for PMSM Drives
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中文总结 AI 辅助
本文针对机器人关节电流控制器的时延与带宽问题，提出任务感知时延扩展离散时间模型，可直接综合带时延保证的离散PI控制器，仿真与实验验证其可降低采样频率和直流母线电压需求。
中文摘要 AI 辅助
现代机器人关节日益增长的动态需求要求电流控制器在宽范围的转速、加速度和转矩工况下实现高带宽，此时通信、计算和离散时间效应已不可忽略。传统永磁同步电机（PMSM）电流控制器通常在连续时间域设计后再进行离散化，使得采样频率和实现时延的影响在很大程度上依赖启发式选择与迭代验证。本文提出一种任务感知、时延扩展的离散时间关节模型，该模型明确考虑了物理通信和计算时延，可在整个工作范围内直接综合具有规定带宽与时延保证的离散PI电流控制器。该框架解析确定了满足指定电机与关节性能所需的最低采样频率、控制器增益和直流母线电压。对一系列动态需求的仿真验证了该方法，与传统基于连续时间的设计相比，其采样频率和直流母线电压需求显著降低。在新开发的定制机器人关节上进行的实验，进一步在实际嵌入式实现条件下验证了所提框架。
英文摘要
The increasing dynamic demands of modern robotic joints require current controllers to achieve high bandwidth over wide operating ranges of speed, acceleration, and torque, where communication, computation, and discrete-time effects can no longer be neglected. Conventional PMSM current controllers are typically designed in continuous time and subsequently discretized, leaving the sampling frequency and the impact of implementation delays largely to heuristic selection and iterative validation. This paper introduces a task-aware, delay-extended discrete-time joint model that explicitly accounts for physical communication and computation delays and enables direct synthesis of a discrete PI current controller with prescribed bandwidth and delay guarantees throughout the operating envelope. The framework analytically determines the minimum required sampling frequency, controller gains, and DC-link voltage needed to satisfy the specified motor and joint performance. Simulations across a range of dynamic requirements validate the methodology and demonstrate substantially reduced sampling-frequency and DC-link-voltage requirements compared with conventional continuous-time-based design. Experiments on a newly developed custom robotic joint further validate the proposed framework under real embedded implementation conditions.
发表机构
- Chair of Robotics and Systems Intelligence（机器人与系统智能主席职位）
- Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与机器智能研究所）
- Technical University of Munich（慕尼黑工业大学）
- Mohamed Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
机构由 AI 辅助整理，请以论文原文为准。
