Optimization design method of controller of multi-degree-of-freedom mechanical arm system

Abstract

The invention discloses an optimization design method of a controller of a multi-degree-of-freedom mechanical arm system. The method is characterized in that an artificial neural network technology and an evolutionary multi-objective optimization algorithm are combined, and an optimal parameter of the multi-variable PID controller is determined according to the multi-degree-of-freedom mechanical arm trajectory tracking control system with nonlinear and uncertainty models. The controller is the multi-variable PID controller, and relatively high tracking precision and good dynamic performance can be obtained when handling the model uncertainty of the mechanical arm system and strong coupling among joints; and in addition, the adopted evolutionary multi-objective optimization algorithm used for optimization can comprehensively consider the tracking precision and dynamic performance of the system and the stability of control, determined the most appropriate controller parameters , has relatively strong universality, and can overcome the defects caused by repeated cut-and-trial of common experiments.

Description

technical field [0001] The invention belongs to the technical field of manipulator control, and relates to an optimal design method for a controller of a multi-degree-of-freedom manipulator system. Background technique [0002] As an advanced mechatronics product, the robotic arm system has been widely and successfully applied in the manufacturing industry. With the rapid development of robot-related technologies, the type and function of the robot arm will continue to develop and improve, and gradually expand its application field. This is a robot product with bright development prospects and wide application prospects. The robotic arm control system is the brain of all robot products, which controls the movement, thinking and behavior of the robot. At present, with the development and progress of intelligent control technology, the intelligence and control performance of robots will inevitably be greatly improved. [0003] With the development and progress of control the...

AI Technical Summary

Problems solved by technology

[0007] First, most of the existing trajectory tracking controller design methods are based on known or approximate mathematical models and adopt traditional PID control strategies to achieve closed-loop control. The parameters of the controller are mostly determined by trial and error, which cannot satisfy Application requirements for high-precision occasions;

[0008] Second, in addition, many methods tend to emphasize the tracking accuracy of the control system during design, ignoring the dynamic performance of the system and the constraints of the servo motor drive system

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