Control method of point cloud polishing remote control system based on sliding mode and adaptive control

By combining sliding mode and adaptive control methods with point cloud data for path planning, a four-channel sliding mode-adaptive controller was designed to solve the problems of dynamic uncertainty and external interference during remote operation grinding, and to achieve efficient and stable robot grinding results.

CN116673819BActive Publication Date: 2026-06-26HUAZHONG UNIV OF SCI & TECH
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Patent Information

Application Number
CN202310392241.4
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2023-04-13
Publication Date
2026-06-26
Estimated Expiration
2043-04-13

AI Technical Summary

Technical Problem

During remote grinding, the uncertainty of the dynamic model parameters of the robot system and the interference of the external environment make it difficult to guarantee the control accuracy and stability. Especially in complex and dangerous working environments, existing technologies are unable to effectively reduce the impact of uncertainties and external interference.

Method used

A four-channel sliding mode-adaptive controller was designed using sliding mode and adaptive control methods. The optimal grinding path was planned by combining point cloud data. The sliding mode controller alleviated force chatter, and the adaptive controller adjusted the control torque in real time to reduce the error caused by dynamic uncertainty.

Benefits of technology

It achieves efficient and stable remote operation control in complex and dangerous environments, improves the accuracy and efficiency of the robot grinding process, reduces system oscillation and instability, and enhances the robot's tracking performance.

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Abstract

The present application belongs to the technical field of grinding processing, and discloses a control method of a point cloud polishing remote control system based on a sliding mode and adaptive control. The control method comprises the following steps: S1, establishing a dynamic equation of the remote control system to obtain control equations of a master robot and a slave robot; S2, performing point cloud scanning on a weld to be polished to obtain corresponding point cloud data, calculating the depth gradient, dexterity and stiffness of each point in the point cloud data, setting a starting point and an ending point of polishing, and planning an optimal polishing path; and S3, inputting the control equations obtained in step S1 and the optimal polishing path obtained in step S2 into a controller to control the slave robot to polish. Through the present application, the accuracy of the control algorithm and the optimization of the point cloud path search are combined, and the efficiency of remote operation control under polishing conditions is improved.
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