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Learning and adaptive controls for coordination of multiple manipulators without knowing physical parameters of an object

机译:在不知道物体的物理参数的情况下,学习和自适应控制在不知道物体的物理参数的情况下

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Learning control and model-based adaptive control schemes are proposed for coordinated control of multiple manipulators when physical parameters such as inertia moments and position of the mass center of a target object are unknown. The proposed schemes are constructed on the equation of motion of the coordinated multiple manipulators, which is derived in terms of joint angle coordinates of manipulators and includes the dynamics of the target object. Since this equation does not explicitly include the position of the mass center of the object, the proposed schemes can achieve exact tracking of given joint angle trajectories and given trajectories of "internal force" without using physical parameters of the manipulators and the object. The effectiveness of the proposed schemes is illustrated by the numerical simulation results on a model of two 3-DOF manipulators holding a single object.
机译:当物理参数如惯性矩和目标对象的质量中心的位置未知时,提出了基于学习控制和基于模型的自适应控制方案。 所提出的方案是在协调多个操纵器的运动的方程上构建的,其在操纵器的接合角坐标方面得到并且包括目标对象的动态。 由于该等式没有明确地包括物体的质量中心的位置,所以所提出的方案可以实现给定的关节角轨迹的精确跟踪,并且在不使用操纵器和物体的物理参数的情况下达到“内部力”的给定轨迹。 所提出的方案的有效性由持有单个物体的两个三维机械手模型的数值模拟结果说明。

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