Robot trace tracking control method and system

Abstract

The present invention discloses a robot trace tracking control method and system. The method comprises the following steps of: the step S100: establishing a dynamical model of an N-degree-of-freedom rigid robotic system; the step S200: according to dynamic characteristics of the robotic system, performing linearization of the dynamical model of the robotic system along an expected trace; and the step S300: taking expected joint angle and joint angular speed of the robotic system as reference input of a robustness adaptive iteration learning controller, taking actual joint angle and joint angular speed of the robotic system as actual input of the controller, generating tracking errors between the actual input and the reference input, and gradually decreasing the tracking errors through iteration calculation of the controller. The robot trace tracking control method and system can perform tracking control of a robot of uncertainty modeling and random disturbance, can improve the rate ofconvergence and the control precision of tracking control and can meet the requirement of the work speed and the precision of the robot.

Description

technical field [0001] The invention relates to the technical field of robot control, in particular to a robot trajectory tracking control method and system. Background technique [0002] With the development of modern industry, the automated production of industrial robots is the mainstream development direction in the field of automation equipment in the future. An industrial robot is a multi-degree-of-freedom positioning device. As a controlled object, it is a highly coupled, multi-input, and multi-output strong nonlinear mechanical device. Like most mechanical systems, the dynamic characteristics of a robotic system can be expressed by a mathematical model represented by its mechanical parameters. The premise is that the structure of the robot system is known, and the system parameters are known precisely. [0003] In fact, due to the influence of changes in the robot's own parameters or the interference of the external environment, it is difficult for us to obtain an ...

AI Technical Summary

Problems solved by technology

[0006] (3) Factors such as random interference in unknown environments, driver saturation, rounding errors, and sampling delays

[0008] 1) Iterative learning control method: The initial control value of traditional iterative control is usually arbitrarily selected a control quantity, and then the control law corrects the control quantity according to the error change, which causes the previous error to be too large, increases the workload of the method, and seriously affects real-time;

[0009] 2) Adaptive control method: Adaptive control can deal with the uncertainties of constant parameters ideally, but it cannot deal with the nonlinear uncertainties of the system, such as unmodeled nonlinear friction and random external disturbances, which may Make the control system unstable so that the precise tracking of the robot cannot be achieved;

This fact makes the controller dependent on the system structure, so current control schemes are very inconvenient to use when the system has an unknown or uncertain structure

Images