Formation-Based Decentralized Iterative Learning Cooperative Impedance Control for a Team of Robot Manipulators
提出一种分散式迭代学习协同阻抗控制架构，使机器人操作手团队在迭代任务中实现期望阻抗模型，即使部分操作手无法直接获取期望角度，且任务可随迭代变化。
We present in this article a new decentralized iterative learning cooperative impedance control (ILCIC) architecture to cooperatively control the impedance for a team of robot manipulators that operate over an iteration domain. With a new definition of the neighborhood impedance error, we propose a novel formation-based cooperative control architecture, so that every manipulator can achieve the desired impedance model, even when some manipulators do not have direct access to the desired angle profiles. Besides, the desired angle profiles as well as the desired impedance model can be iteration varying, which is an important consideration when the team needs to execute different tasks in different iterations. With rigorous mathematical analysis, we show that each manipulator’s impedance error can uniformly converge to zero as the iteration index increases to infinity. A simulation study is discussed in order to further illustrate the effectiveness of the discussed algorithm.