首页> 外文会议>ASME International Mechanical Engineering Congress and Exposition >POSITION CONTROL OF A MANIPULATOR ROBOTIC ARM CONSIDERING FLEXIBLE JOINTS DRIVEN BY A DC MOTOR AND A CONTROLLED TORQUE BY A MR-BRAKE
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POSITION CONTROL OF A MANIPULATOR ROBOTIC ARM CONSIDERING FLEXIBLE JOINTS DRIVEN BY A DC MOTOR AND A CONTROLLED TORQUE BY A MR-BRAKE

机译:考虑到DC电动机驱动的柔性接头和MR制动器的控制扭矩,定位控制机器机器人机器人的位置控制

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This paper presents a two-degree-of-freedom robotic arm design with flexible joints driven by a DC Motor and controlled by a Magnetorheological (MR) Brake, considering a feedback control. The MR Brake is used to provide adjustable constraints in motion of the manipulator and compensate overshoot by interactions between the robot's links and flexible joints of the motor drive mechanism. The torque of the MR Brake is obtained by the Radial Basis Function Neural Networks (RBFNN), which is a widely used class of neural networks for prediction or approximation of function. The RBFNN provides the nonlinear curve of hysteresis of MR brake to use torque. Two controllers were proposed to control the manipulator. The first one is obtained by feedback linearization control with the objective to remove the non-dependent terms of the state space equation. The second one is the feedback control obtained using the State-Dependent Riccati Equation (SDRE) with the objective of controlling the position of the manipulator and the torque applied on the MR brake. The numerical simulation results showed that the proposed control using both signal feedback linearization control and a feedback control signal by a DC Motor and MR Brake is effective to control the flexible joint manipulators.
机译:本文介绍了一种自由度机器人臂设计,具有由直流电动机驱动的柔性接头,并考虑反馈控制,由磁流变(MR)制动器控制。 MR制动器用于在操纵器的运动中提供可调节的约束,并通过机器人链路和电动机驱动机构的柔性接头之间的相互作用来补偿过冲。 MR制动器的扭矩由径向基函数神经网络(RBFNN)获得,这是一种广泛使用的用于预测或近似功能的神经网络。 RBFNN提供MR制动器滞后的非线性曲线以使用扭矩。建议两个控制器控制操纵器。通过反馈线性化控制获得第一个,目的是去除状态空间方程的非依赖性术语。第二个是使用状态相关的Riccati等式(SDRE)获得的反馈控制,其目的是控制操纵器的位置和施加在MR制动器上的扭矩。数值模拟结果表明,使用信号反馈线性化控制和DC电动机和MR制动器的反馈控制信号的所提出的控制对于控制柔性联合机械手是有效的。

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