IL-ACT：面向30吨级挖掘机的自适应笛卡尔跟踪控制模仿学习

中文摘要 AI 辅助

自主挖掘机控制面临运动学耦合、执行滞后和不确定性的挑战。我们提出了模仿学习与自适应笛卡尔跟踪（IL-ACT），一种面向30吨级挖掘机的新型运动控制框架。一个基于操作员演示预训练的锚定14输入模仿策略生成标称关节速率；自适应笛卡尔反馈和门控增益/偏置估计在停止距离调节器约束关节参考生成之前对这些指令进行修正。Simscape评估涵盖100个连续目标和螺旋、八字形及圆角栅格跟踪，包括在液压响应和传感条件下，跨三个训练种子、两种初始化和速度的88次额外运行。与Teacher+ACT相比，IL-ACT在两种响应条件下均以更短持续时间和更低终端误差完成所有目标。遥测初始化的IL-ACT在所有24个八字形和圆角栅格种子比较中降低了RMSE，并将附加载荷螺旋平均RMSE降低约29%。原始螺旋RMSE也优于仅IL和PID。在共享传感器噪声实现下，遥测初始化的IL-ACT比Teacher+ACT平均RMSE低27.67%；启用估计相对于冻结估计器使平均RMSE降低22.44%。预训练权重的影响仍然混合，原始教师比较表现出螺旋RMSE-最大误差权衡。分析确立了有界自适应状态和笛卡尔反馈，参考可采纳性以调节器可行性为条件。

英文摘要

Autonomous excavator control is challenged by coupled kinematics, actuation lag, and uncertainty. We propose imitation learning and adaptive Cartesian tracking (IL-ACT), a novel motion control framework for a 30-ton-class excavator. An anchored, 14-input imitation policy pretrained on operator demonstrations generates nominal joint rates; adaptive Cartesian feedback and gated gain/bias estimation correct these commands before a stopping-distance governor constrains joint-reference generation. Simscape evaluation covers 100 sequential goals and spiral, figure-eight, and rounded-raster tracking, including 88 additional runs across three training seeds, two initializations, and speeds, under hydraulic response and sensing conditions. Compared with Teacher+ACT, IL-ACT completes all goals with shorter duration and lower terminal errors under both response conditions. Telemetry-initialized IL-ACT lowers RMSE in all 24 figure-eight and rounded-raster seed comparisons and lowers additional-load spiral mean RMSE by approximately 29%. Original spiral RMSE also improves over IL-only and PID. Under a shared sensor-noise realization, telemetry-initialized IL-ACT achieves 27.67% lower mean RMSE than Teacher+ACT; enabling estimation reduces mean RMSE by $22.44\%$ relative to the frozen estimator. Pretrained-weight effects remain mixed, and the original teacher comparison exhibits a spiral RMSE--maximum-error tradeoff. Analysis establishes bounded adaptive states and Cartesian feedback, with reference admissibility conditional on governor feasibility.

发表机构

• Tampere University（坦佩雷大学）
• Korea University（高丽大学）
• Ajou University（亚洲大学）

机构由 AI 辅助整理，请以论文原文为准。