面向精度评价的并联机床参数辨识技术

基于并联机床外部标定,推导出辨识方程的残差近似等于标定后的位姿误差。为克服最小二乘法存在较大残差的缺陷,面向精度评价,提出最小最大优化的参数辨识技术,以残差最大绝对值最小为优化目标,直接控制误差范围。标定实例验证了最小二乘法缺陷的实际存在和最小最大优化抑制较大残差的效果。该参数辨识技术直接联系运动学标定和标定后的精度评价,简单有效的提高并联机床的整体精度。

PRECISION EVALUATION ORIENTED PARAMETER IDENTIFICATION OF PKM TOOLS

During the external kinematic calibration of parallel kinematic machine (PKM) tools, in order to eliminate a few larger residual errors which possibly lie in the parameter identification based on the least square method, the min-max optimization method is presented. From the engineering sense of the residual errors of parameter identification, precision evaluation is oriented and min-max method directly controls the scope of residual errors. The experiment proves that the limitation of the least square method exists and the min-max method is effective to eliminate the limitation. The min-max method of parameter identification directly connects precision evaluation with kinematic calibration and can improve the total precision of PKM tools simply and cheaply.