Design method and system for industrial robot based on four-axis iterative learning control

Abstract

The invention discloses a four-axis iterative learning control industrial robot design method. A three-dimensional part that needs pre-forming is converted into a homogenous curve cluster trajectory,and then curves are sequentially extracted as desired trajectories of system tracking, so that the iterative learning control is separately applicable and therefore is effectively applicable to such trajectory groups so as to complete the machining of the three-dimensional part. Through the effective inheritance of the control information obtained from learning the previous desired trajectory, sothat the initial iterative control information when the current desired trajectory is tracked is no longer learned from scratch, the learning speed of the system is greatly increased, the number of iterations is reduced, and thus the work efficiency is substantially improved. Moreover, the industrial robot system applying the design method can machine the three-dimensional parts whose base trajectory cannot be machined by a conventional rotary body device, greatly improves the diversity of industrial parts, and is very suitable for the production of individualized parts in the industry. .

Description

technical field [0001] The invention belongs to the technical field of trajectory tracking control, and in particular relates to an industrial robot design method and an industrial robot system for four-axis iterative learning control. Background technique [0002] Industrial robot technology is currently one of the research hotspots in the scientific and industrial circles, and the trajectory tracking of its operating terminal has always been the core issue of research. Iterative learning control (ILC) is suitable for a class of controlled objects with repetitive operation characteristics. Its task is to find the ideal control input so that the actual output trajectory of the controlled system can achieve zero error completeness along the entire expected output trajectory in a limited time interval. Tracking, and the entire control process requires quick completion. [0003] Although the iterative learning control method can realize the complete tracking of the expected tr...

AI Technical Summary

Problems solved by technology

[0003] Although the iterative learning control method can realize the complete tracking of the expected trajectory, the existing methods require the expected trajectory to be strictly consistent. As long as the expected trajectory changes arbitrarily, the control information of the iterative learning control will be relearned from zero, and the previously learned control information can not be effectively used

[0004] For the research on the trajectory tracking problem, the existing literature mainly focuses on a single trajectory, and has not analyzed the trajectory group with certain attributes.

Traditional iterative learning control has three axes, which are time axis, iteration axis and amplitude axis. For the tracking problem where the expected trajectory is a single trajectory, the traditional ILC with three axes can be applied. However, for the trajectory where the expected trajectory is composed of several trajectories The tracking problem of the group, the traditional ILC with three axes can no longer be effectively applied

[0005] Traditional gyrotron equipment can only process parts whose base surface is a perfect circle trajectory, but cannot process three-dimensional parts whose base surface is an arbitrary shape

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