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Robust task-space control of robot manipulators using differential equations for uncertainty estimation

机译:使用微分方程进行不确定性估计的机器人机械臂的鲁棒任务空间控制

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摘要

Most control algorithms for rigid-link electrically driven robots are given in joint coordinates. However, since the task to be accomplished is expressed in Cartesian coordinates, inverse kinematics has to be computed in order to implement the control law. Alternatively, one can develop the necessary theory directly in workspace coordinates. This has the disadvantage of a more complex robot model. In this paper, a robust control scheme is given to achieve exact Cartesian tracking without the knowledge of the manipulator kinematics and dynamics, actuator dynamics and nor computing inverse kinematics. The control design procedure is based on a new form of universal approximation theory and using Stone-Weierstrass theorem, to mitigate structured and unstructured uncertainties associated with external disturbances and actuated manipulator dynamics. It has been assumed that the lumped uncertainty can be modeled by linear differential equations. As the method is Model-Free, a broad range of manipulators can be controlled. Numerical case studies are developed for an industrial robot manipulator.
机译:刚性链接电动机器人的大多数控制算法在关节坐标中给出。但是,由于要完成的任务用笛卡尔坐标表示,因此必须计算逆运动学以实现控制律。或者,可以直接在工作空间坐标中开发必要的理论。这具有更复杂的机器人模型的缺点。在本文中,给出了一种鲁棒的控制方案,以实现精确的笛卡尔跟踪,而无需了解机械手的运动学和动力学,执行器动力学以及也不计算逆运动学。控制设计程序基于一种新形式的通用逼近理论,并使用Stone-Weierstrass定理,以减轻与外部干扰和机械手动力学相关的结构性和非结构性不确定性。假设可以通过线性微分方程对集总不确定度进行建模。由于该方法是无模型的,因此可以控制各种各样的操纵器。针对工业机器人操纵器开发了数值案例研究。

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