用准周期高斯过程提升机器人重复任务的控制精度与速度
Quasi-Periodic Gaussian Process Predictive Iterative Learning Control
- 用准周期高斯过程建模迭代中的干扰和漂移
- 计算复杂度从O(i²p³)降为O(p³)，支持持续学习
- 适用于自动驾驶、机械臂等重复性运动系统
重复运动任务在机器人中常见，但环境变化和设备磨损会导致性能下降。迭代学习控制（ILC）通过利用前次迭代信息补偿未来迭代中的预期误差来提升性能。本文将准周期高斯过程（QPGP）引入预测型ILC框架，用于建模和预测跨迭代的扰动与漂移。基于最新的结构方程形式，该方法将推断复杂度从O(i²p³)降至O(p³)，其中p为单次迭代点数，i为总迭代次数，尤其适用于较大i的情况。该形式还实现了无信息损失的参数估计，使高斯过程在控制环内持续学习成为可能。通过预测下一次迭代的误差分布而非仅依赖历史误差，控制器实现更快收敛，并在时变扰动下保持鲁棒性。在自动驾驶轨迹跟踪、三连杆机械臂及真实Stretch机器人实验中，相比标准ILC和传统基于高斯过程的预测ILC，本方法收敛更快，抗扰能力更强，且计算开销更低，验证了其在多种重复动力系统中的实用性。
原文摘要 · Abstract (English)
Repetitive motion tasks are common in robotics, but performance can degrade over time due to environmental changes and robot wear and tear. Iterative learning control (ILC) improves performance by using information from previous iterations to compensate for expected errors in future iterations. This work incorporates the use of Quasi-Periodic Gaussian Processes (QPGPs) into a predictive ILC framework to model and forecast disturbances and drift across iterations. Using a recent structural equation formulation of QPGPs, the proposed approach enables efficient inference with complexity $\mathcal{O}(p^3)$ instead of $\mathcal{O}(i^2p^3)$, where $p$ denotes the number of points within an iteration and $i$ represents the total number of iterations, specially for larger $i$. This formulation also enables parameter estimation without loss of information, making continual GP learning computationally feasible within the control loop. By predicting next-iteration error profiles rather than relying only on past errors, the controller achieves faster convergence and maintains this under time-varying disturbances. We benchmark the method against both standard ILC and conventional Gaussian Process (GP)-based predictive ILC on three tasks, autonomous vehicle trajectory tracking, a three-link robotic manipulator, and a real-world Stretch robot experiment. Across all cases, the proposed approach converges faster and remains robust under injected and natural disturbances while reducing computational cost. This highlights its practicality across a range of repetitive dynamical systems.
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