基于无模型自适应控制的视觉伺服

Visual Servo Based on Model-free Adaptive Control

传统的机器人视觉伺服控制技术需要已知机器人精确的动力学和运动学模型以及机器人的手-眼参数。然而,由于机器人建模、手-眼标定等过程存在一定误差,因此很难精确获得视觉伺服控制模型,从而影响机器人视觉伺服系统的精度和收敛速度。针对这一难题,本文提出一种基于无模型自适应控制方法(MFAC)的机器人视觉伺服技术。利用视觉伺服系统的输入与输出数据,实现自适应视觉伺服控制,即通过MFAC在线估计机器人伺服控制器中的雅各比矩阵,并结合滑模控制器,实现机器人对目标的快速精确跟踪。实验结果表明,本文提出的方法在系统参数变化引起的未知扰动情况下仍能保证伺服控制器平稳收敛,并且能够减小视觉跟踪误差。

The traditional robot visual servo control technology requires accurate dynamics and kinematics models of known ro bots and the calibration of camera.However,due to the errors in the robot modeling and camera calibration,it is difficult to accu rately build the error model,which affects the positioning accuracy and convergence speed of the robot vision servo system.To solve this problem,this paper proposes a robot vision servo technology based on Model-free Adaptive Control(MFAC).Using the input and output data of the system,this paper realizes adaptive visual servo control.Namely by the Jacobian matrix in the MFAC online estimation robot servo controller and combining with sliding mode controller,this paper achieves the precise track ing task to targets.The results of simulation experiments show that the proposed method can ensure the smooth convergence of the servo controller under the unknown disturbance caused by the change of system parameters and reduce the system positioning error.