Adaptive Learning-Based Distributed Control of Cooperative Robot Arm Manipulation for Unknown Objects
提出一种多机器人系统分布式协作控制方案，通过在线学习模块估计未知负载动力学，并设计轨迹跟踪协议，使机器人能在未知抓取点和外部扰动下稳定操作物体。
This article proposes a distributed cooperative manipulation control scheme for multirobot systems to track reference trajectories with unknown payload dynamics, grasp positions, and external disturbances. An online learning module is established to estimate the payload dynamics. Then a wrench-synthetic trajectory tracking control protocol is thereby developed to manipulate an object under unknown external disturbances no matter where the grasping points are. Moreover, sufficient conditions are derived to guarantee the uniform boundedness of the tracking errors of the closed-loop cooperative manipulation system. Finally, numerical simulations are conducted to substantiate the effectiveness of the proposed cooperative manipulation control scheme.