发表机构
California Institute of Technology (Caltech); Jet Propulsion Laboratory (JPL)（加州理工学院; 喷气推进实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种统计学习框架，通过硬EM和卡尔曼平滑学习收缩扰动表示，结合贝叶斯滤波实现复合自适应跟踪控制，实验验证了预测能力和性能提升。
AI 中文摘要
我们提出了一种用于动态耦合扰动下复合自适应跟踪控制的表示学习框架。该框架将经典扰动适应控制（DAC）与最近的最后一层自适应扰动抑制方法联系起来。具体而言，我们引入了一种统计上合理的硬期望最大化（hard-EM）程序，在硬E步中使用卡尔曼平滑器，以识别潜在演化一致收缩的扰动动态表示。学习到的表示从测量的植物特征和控制输入中演化出潜在扰动激励状态，并将该状态解码为作用于名义植物的时变扰动，从而将先前的“固定衰减”最后一层自适应方法扩展为学习型、预测性的DAC风格公式。结合对学习到的潜在状态的贝叶斯滤波，该表示产生了一种具有预测能力和可证明指数收敛到有界邻域的复合自适应跟踪控制器。我们在携带液体晃荡罐和摆锤负载的湿滑地面车辆上实验验证了我们的方法，并进一步在耦合Duffing振荡器系统上评估其鲁棒性。在这两种设置中，相对于固定衰减表示学习消融、LTI扰动适应基线和基于模型的PD基线，该方法实现了准确的扰动预测和改善的整体跟踪性能。
英文摘要
We present a representation-learning framework for composite adaptive tracking control under dynamically coupled disturbances. The framework connects classical disturbance-accommodating control (DAC) to recent last-layer adaptive disturbance-rejection methods. Specifically, we introduce a statistically principled hard expectation-maximization (hard-EM) procedure, with a Kalman smoother in the hard E-step, to identify dynamical representations of disturbance whose latent evolution is uniformly contractive. The learned representation evolves a latent disturbance-excitation state from measured plant features and control inputs and decodes that state into the time-varying disturbance acting on the nominal plant, thereby extending prior "fixed-decay" last-layer adaptive methods to a learned, predictive DAC-style formulation. Combined with Bayesian filtering of the learned latent state, this representation yields a composite adaptive tracking controller with predictive capability and provable exponential convergence to a bounded neighborhood. We validate our approach experimentally on a slippery ground vehicle carrying a liquid-sloshing tank and a pendulum load, and we further assess its robustness on a system of coupled Duffing oscillators. Across both settings, the method achieves accurate disturbance prediction and improved overall tracking performance relative to fixed-decay representation-learning ablations, LTI disturbance-accommodating baselines, and model-based PD baselines.
Comments9 pages, including an additional one-page appendix in this arXiv version. Accepted to the 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2026)
