针对四足机器人周期性干扰，提出轻量级自适应控制方法
Adaptive MPC-based quadrupedal robot control under periodic disturbances
- 用简化动力学建模实现周期干扰的在线估计
- 实验显示相比静态补偿，轨迹跟踪精度显著提升
- 适合需要抗周期扰动的机器人控制场景
近年来，自适应控制在四足机器人参考轨迹跟踪中的进展使其能在复杂条件下执行运动任务。已有方法可估计外部干扰的力与力矩，但针对周期性干扰在四足机器人上的应用尚未被专门解决。本文提出一种基于模型预测控制（MPC）的轻量级回归器，利用简化机器人动力学，对周期性干扰的幅值和频率进行实时估计。实验表明，该方法在轨迹跟踪性能上优于基线静态干扰补偿策略。所有源代码、仿真设置及计算脚本均已公开于GitHub：https://github.com/aidagroup/quad-periodic-mpc。
原文摘要 · Abstract (English)
Recent advancements in adaptive control for reference trajectory tracking enable quadrupedal robots to perform locomotion tasks under challenging conditions. There are methods enabling the estimation of the external disturbances in terms of forces and torques. However, a specific case of disturbances that are periodic was not explicitly tackled in application to quadrupeds. This work is devoted to the estimation of the periodic disturbances with a lightweight regressor using simplified robot dynamics and extracting the disturbance properties in terms of the magnitude and frequency. Experimental evidence suggests performance improvement over the baseline static disturbance compensation. All source files, including simulation setups, code, and calculation scripts, are available on GitHub at https://github.com/aidagroup/quad-periodic-mpc.
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