Mechanical arm PID control method based on BP neural network and related device

Through the BP neural network-based robot PID control method, the PID control parameters are dynamically adjusted through flexible arm mode function representation and dynamic analysis, which solves the accuracy problem of traditional control methods under complex conditions and improves the robotic arm control system performance and stability.

CN119217381APending Publication Date: 2024-12-31YUNNAN POWER GRID CO LTD NUJIANG POWER SUPPLY BUREAU
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Patent Information

Application Number
CN202411666300.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-11-20
Publication Date
2024-12-31

AI Technical Summary

Technical Problem

Traditional PID control methods for robotic arm are difficult to achieve accurate control under complex external conditions, especially few researches on space soft robotic arms, and traditional PID controller parameters are difficult to adapt to external interference and dynamic changes in the system.

Method used

The PID control method of the robot arm based on BP neural network is used to dynamically adjust the PID control parameters to adapt to the motion characteristics of the robot arm through the flexible arm elastic displacement mode function representation, dynamic analysis, motion modeling and adaptive PID control.

Benefits of technology

More accurate PID control parameters are achieved, the performance and stability of the robotic arm control system are improved, and the ability to better adapt to complex external conditions and dynamic changes in the system are better adapted to.

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Abstract

The invention relates to the technical field of data processing, and discloses a mechanical arm PID control method based on a BP neural network and a related device.The mechanical arm PID control method comprises the steps that flexible arm elastic displacement modal function representation is conducted on a flexible space mechanical arm system, and a flexible arm modal function is obtained; kinetic analysis is conducted on the flexible arm modal function, and a mechanical arm kinetic model is obtained; mechanical arm motion modeling is conducted on the mechanical arm dynamic model based on a back propagation BP neural network, and a mechanical arm motion model is obtained; according to the mechanical arm motion model, self-adaptive PID control processing is carried out, and a reference PID control parameter is obtained; the mechanical arm is controlled to operate according to the reference PID control parameters, and feedback parameters are obtained; according to the feedback parameters, the reference PID control parameters are adjusted, more accurate target PID control parameters can be obtained, a better control effect can be achieved, and the performance of the mechanical arm control system can be improved.
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