迭代学习控制
杠杆(统计)
计算机科学
弹道
运动学
机器人
控制理论(社会学)
任务(项目管理)
机械手
最优控制
航程(航空)
控制(管理)
控制工程
人工智能
工程类
数学优化
数学
系统工程
物理
经典力学
天文
航空航天工程
作者
Matthijs van de Vosse,Tyler Toner,Maxwell Wu,Dawn M. Tilbury,Kira Barton
标识
DOI:10.1109/case56687.2023.10260418
摘要
Industrial manipulators are deployed for a range of repetitive tasks in cluttered environments in which the robot must rapidly execute safe trajectories. While nominal robot models exist, true dynamic models of deployed manipulators are typically unavailable. This paper addresses the problem of generating dynamically feasible, collision-free, time-optimal kinematic reference signals for redundant manipulators with unknown dynamics. A novel economic iterative learning control approach is developed to leverage repeated task executions to learn a time-optimal control signal for an uncertain robot model. Simulation results demonstrate the performance of the approach for a 7-DOF manipulator. An experimental analysis is performed to understand the impact of the initial reference trajectory on converged performance.
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