李陇南, 黄攀峰, 马志强. 基于时变输出约束的机器人遥操作有限时间控制方法[J]. 机器人, 2022, 44(1): 19-34, 44. DOI: 10.13973/j.cnki.robot.210234
引用本文: 李陇南, 黄攀峰, 马志强. 基于时变输出约束的机器人遥操作有限时间控制方法[J]. 机器人, 2022, 44(1): 19-34, 44. DOI: 10.13973/j.cnki.robot.210234
LI Longnan, HUANG Panfeng, MA Zhiqiang. Finite-time Control Method for Robot Teleoperation Based on Time-varying Output Constraints[J]. ROBOT, 2022, 44(1): 19-34, 44. DOI: 10.13973/j.cnki.robot.210234
Citation: LI Longnan, HUANG Panfeng, MA Zhiqiang. Finite-time Control Method for Robot Teleoperation Based on Time-varying Output Constraints[J]. ROBOT, 2022, 44(1): 19-34, 44. DOI: 10.13973/j.cnki.robot.210234

基于时变输出约束的机器人遥操作有限时间控制方法

Finite-time Control Method for Robot Teleoperation Based on Time-varying Output Constraints

  • 摘要: 受操作时间窗口和工作空间的限制,空间遥操作任务需要在有限时间内完成,同时确保末端执行器满足物理约束。此外,时延和外部扰动使不确定遥操作系统的稳定性和控制性能受到严重影响。为此,本文提出了一种基于时变输出约束的机器人遥操作有限时间控制方法。首先,利用积分障碍李雅普诺夫函数处理操作空间的时变约束问题,实用有限时间李雅普诺夫稳定定理保证了系统的快速稳定性。然后,利用神经网络估计环境力以及消解模型不确定性带来的影响,利用鲁棒项补偿神经网络的估计偏差和消解未知外部扰动的影响。最后,在Matlab/Simulink环境下同其他算法进行仿真对比,并在地面实验平台上验证了该算法,理论仿真和实验结果表明该方法进一步提高了误差收敛速率和收敛精度,且系统的输出不会超出预先设定的时变边界。

     

    Abstract: Limited by the operation time window and working space, the space teleoperation tasks need to be completed in a finite time while ensuring that the end effector meets the physical constraints. In addition, time delay and external disturbance seriously affect the stability and control performance of uncertain teleoperation system. Therefore, a finitetime control method for robot teleoperation based on time-varying output constraints is proposed. Firstly, the integral barrier Lyapunov function is used to deal with the time-varying constraints of the operation space, and the rapid stability of the system is guaranteed by the practical finite-time Lyapunov stability theorem. Then, the neural network is utilized to estimate the environment force and resolve the impact of model uncertainty, and the robust term is used to compensate for the estimation bias of the neural network and eliminate the influence of unknown external disturbances. Finally, the proposed algorithm is compared with other algorithms in Matlab/Simulink simulation environments and verified on the ground experiment platform. The results of theoretical simulation and experiment show that the proposed method can further improve the error convergence rate and convergence accuracy, and the output of the system never violates the prescribed time-varying boundary.

     

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