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首页> 外文期刊>Progress in Artificial Intelligence >An Adaptive Second Order Sliding Mode Inverse Kinematics Approach for Serial Kinematic Chain Robot Manipulators
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An Adaptive Second Order Sliding Mode Inverse Kinematics Approach for Serial Kinematic Chain Robot Manipulators

机译:串行链轮机器人操纵器的自适应二阶滑动模式逆运动学方法

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摘要

The problem of inverse kinematics is essential to consider while dealing with the robot's mechanical structure in almost all applications. Since the solution of the inverse kinematics problem is very complex, many research efforts have been working towards getting the approximate solution of this problem. However, for some applications, working with the approximate robot's model is neither sufficient nor efficient. In this paper, an adaptive inverse kinematics methodology is developed to solve the inverse kinematics problem in such a way that compensate for unknown uncertainty in the Jacobian matrix of the serial kinematic chain robot manipulators. The proposed methodology is based on continuous second order sliding mode strategy (CSOSM-AIK). The salient advantage of the CSOSM-AIK approach is that it does not require the availability of the kinematics model or Jacobian matrix of the robot manipulators from joint space variables to Cartesian space variables. The global stability of the closed-loop system with CSOSM-AIK methodology is proven using the Lyapunov theorem. In order to demonstrate the robustness and effectiveness of the proposed methodology, some simulations are conducted.
机译:在几乎所有应用中处理机器人的机械结构时,逆运动学的问题对于考虑至关重要。由于反向运动学问题的解决方案非常复杂,因此许多研究努力一直在努力获得这个问题的大致解决方案。但是,对于某些应用程序,使用近似机器人的模型既不足够也不高效。在本文中,开发了一种自适应逆运动学方法,以解决逆运动学问题,这种方式可以补偿串行链链机器人操纵器的雅各比矩阵中的未知不确定性。所提出的方法基于连续的二阶滑动模式策略(CSOSM-AIK)。 CSOSM-AIK方法的突出优点是它不需要从接合空间变量与机器人操纵器的运动学模型或雅各比矩阵的可用性到笛卡尔空间变量。使用Lyapunov定理证明了具有CSOSM-AIK方法的闭环系统的全局稳定性。为了证明所提出的方法的鲁棒性和有效性,进行了一些模拟。

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