Robotic juggling by iterative learning control using optimization

H. Miyashita, T. Yamawaki, M. Yashima
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引用次数: 1

Abstract

This paper proposes the control strategy for throwing as one of the dynamic manipulation. In dynamic manipulation, the robot's motion is dynamic and quick and there is no constant contact state. The dynamic manipulation has strong nonlinearity between control input and output, which often makes the manipulation unstable. And modeling errors affect the success of its manipulation task seriously. The dynamic manipulation requires more powerful actuator than the static manipulation.We propose the control method using the learning control that can consider the modeling error problem, the performance limits of the actuator and the stability of the learning control.
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机器人杂耍的迭代学习优化控制
本文提出了将投掷作为一种动态操作的控制策略。在动态操作中,机器人的运动是动态的、快速的,不存在恒定的接触状态。动态控制在控制输入和输出之间具有很强的非线性,这往往使动态控制变得不稳定。而建模误差严重影响其操作任务的顺利完成。动态操作比静态操作需要更强大的致动器。我们提出了一种基于学习控制的控制方法,该方法可以考虑建模误差问题、执行器的性能限制和学习控制的稳定性。
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