甄冒发, 徐振峰, 谢宇. 多自由度机器人惯性误差反馈融合闭环控制[J]. 微电子学与计算机, 2020, 37(12): 77-80.
引用本文: 甄冒发, 徐振峰, 谢宇. 多自由度机器人惯性误差反馈融合闭环控制[J]. 微电子学与计算机, 2020, 37(12): 77-80.
ZHEN Mao-fa, XU Zhen-feng, XIE Yu. A closed-loop control method for inertial error feedback fusion of multi-degree-of-freedom robot[J]. Microelectronics & Computer, 2020, 37(12): 77-80.
Citation: ZHEN Mao-fa, XU Zhen-feng, XIE Yu. A closed-loop control method for inertial error feedback fusion of multi-degree-of-freedom robot[J]. Microelectronics & Computer, 2020, 37(12): 77-80.

多自由度机器人惯性误差反馈融合闭环控制

A closed-loop control method for inertial error feedback fusion of multi-degree-of-freedom robot

  • 摘要: 多自由度机器人受到环境突变性以及自身构件扰动的影响, 导致控制失稳.为了提高多自由度机器人的稳定性, 提出基于误差反馈融合跟踪的多自由度机器人闭环控制方法.构建多自由度机器人惯性跟踪的参数辨识模型, 结合多因素约束参数融合方法进行多自由度机器人的驱动机构部件柔性控制, 建立机器人惯性误差反馈的自适应控制律, 构建机器人的受力参数优化分析模型和动力学模型, 结合误差反馈自适应修正和惯性补偿方法进行多自由度机器人闭环控制过程中的参数自适应寻优, 根据参数寻优结果进行机器人的位姿自适应调整, 实现机器人的稳定性控制.仿真结果表明, 采用该方法进行多自由度机器人控制的误差较小, 控制稳定性和鲁棒性较强, 可以快速收敛到稳定状态.

     

    Abstract: The multi-degree-of-freedom robot is affected by the abrupt change of environment and disturbance of its own components, which leads to instability of control. In order to improve the stability of the multi-degree-of-freedom robot, a closed-loop control method of multi-degree-of-freedom robot based on error feedback fusion tracking is proposed. A parameter identification model for inertial tracking of a multi-degree-of-freedom robot is established, flexible control of drive mechanism components of the multi-degree-of-freedom robot is carried out by combining a multi-factor constraint parameter fusion method, an adaptive control law for inertial error feedback of the robot is established, a mechanical parameter optimization analysis model and a dynamic model of the robot are established, parameter adaptive optimization in the closed-loop control process of the multi-degree-of-freedom robot is carried out by combining an error feedback adaptive correction method and an inertial compensation method, and the robot position and posture adaptive adjustment is carried out according to the parameter optimization result, so that the stability control of the robot is realized. The simulation results show that the control error of the multi-degree-of-freedom robot using this method is small, the control stability and robustness are strong, and it can quickly converge to a stable state.

     

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