Optimal design method of adaptive fuzzy controller for multi-degree-of-freedom manipulator system

Abstract

The invention discloses an optimal design method for a controller of a multi-degree-of-freedom manipulator system, which combines artificial neural network technology and an evolutionary multi-objective optimization algorithm, and aims at the trajectory of a multi-degree-of-freedom manipulator with nonlinear and uncertain models tracking control system to determine the optimal parameters of the multivariable PID controller; the controller of the present invention is a multivariable PID controller, which can handle the uncertainty of the mechanical arm system model and the strong coupling phenomenon between the joints, and obtain higher Tracking accuracy and good dynamic performance; In addition, the evolutionary multi-objective optimization algorithm adopted in the optimization of the present invention can comprehensively consider the tracking accuracy and dynamic performance of the system and the stability of control, determine the most suitable controller parameters, and have a strong Versatility can also overcome the disadvantages brought about by the trial and error method of common experiments.

Description

technical field [0001] The invention relates to the technical field of controller design, in particular to an optimal design method for an adaptive fuzzy controller of a multi-degree-of-freedom manipulator system. Background technique [0002] As an advanced mechatronic product, the robotic arm system has been widely and successfully applied in many fields such as manufacturing, medical treatment, military, aerospace, and entertainment services. At present, the technology related to robots has been developed rapidly. The types and functions of robotic arms have been continuously enriched and improved, and they have been widely used in all aspects of modern industrial fields. The robotic arm control system is the brain of all robotic products. It controls the Movement, thinking and behavior of robots. With the evolution of technology and the development of society, higher and higher requirements are placed on the performance of various robotic arm products. With the continu...

AI Technical Summary

Problems solved by technology

But for the actual manipulator system, the interference signals from inside or outside the system are either unknown or difficult to detect

Therefore, it is difficult to design the controller by applying the traditional servo control theory, and it is impossible to guarantee the control quality of the control system.

[0006] Therefore, there are some deficiencies in the existing design methods of trajectory tracking control systems of manipulators: most of the existing controller design methods are based on known or approximate mathematical models and adopt traditional PID control strategies to achieve closed-loop control; In addition, when determining the controller parameters, only the tracking accuracy of the system is often considered, ignoring the dynamic performance of the system and the constraints of the servo motor drive system

When there are large modeling errors, parameter changes and various unknown disturbances in the control system, there is a large deviation between the actual control performance and the expected performance, which can only meet the requirements of tracking control accuracy under general conditions

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