Method for correcting track errors generated during iterative learning of industrial robot

Abstract

A method for correcting track errors generated during iterative learning of an industrial robot comprises the following steps that firstly, a specific controlled object is determined, an electric current loop or a speed closed loop is adopted as the controlled object, and optimizing and setting of control parameters are carried out on a whole control loop; and secondly, according to the formula (please see the specification), the learning gain phi is changed, the position of the starting point of a N(z) Nyquist curve and the amplitude of the curve are adjusted, introduced offline lead compensation factors enable the N(z) Nyquist curve to achieve translation, more curves fall into a unit circle, gamma=1,2,3...n, and in the formula, q is the feedback gain, and gamma is the number of samplingperiods. According to the method, the design of a robot iterative learning controller is provided according to the characteristic that the industrial robot operates on the same track multiple times,the robot has the self correction capacity, the experience in the previous operating track process is gained to guide operating of subsequent tracks, the more the robot operates, the higher the accuracy is, following errors are reduced greatly, and the accuracy of track operating is improved.

Description

technical field [0001] The invention relates to the field of motion control, in particular to a method for iteratively learning and correcting trajectory errors of industrial robots. Background technique [0002] Industrial robots have been widely used in the industrial field due to their high versatility, environmental adaptability, durability and reliability, and have made outstanding contributions to improving industrial production efficiency, improving labor conditions and realizing high industrial automation. Among them, one of the important application scenarios of industrial robots is the tracking of fixed trajectories, such as welding robots, spraying robots, etc., which require the robot end effector to run accurately according to the established path. And the same established path often requires the robot to run repeatedly. For example, the welding robot on the automobile production line needs to weld each arriving automobile body weld, while the automobile weld t...

AI Technical Summary

Problems solved by technology

like figure 2 As shown, the error is gradually converged during the first 20 iterations, but then the error diverges rapidly, but finally after enough runs, the error tends to converge

Obviously, the divergence of the error will lead to the failure of the learning convergence algorithm, and it is necessary to analyze the cause of the divergence and propose a solution

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