Rope parallel robot vibration suppression control method based on mechanism model and data driving

Through a method based on mechanism model and data-driven, combined with deep learning model and sliding mode controller, the vibration problem caused by rope-sleeved robots is solved, and the effect of rapid vibration suppression and improved operational accuracy is achieved.

CN119734263AActive Publication Date: 2025-04-01CHENGDU-CHONGQING SHUANGCHENG ECONOMIC CIRCLE (LUZHOU) ADVANCED TECH RES INST +3
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Patent Information

Application Number
CN202411883296.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-19
Publication Date
2025-04-01
Estimated Expiration
2044-12-19

AI Technical Summary

Technical Problem

Due to the flexibility and unidirectional force limitation of the rope structure, cable parallel robots are susceptible to external interference and vibration, affecting the operation accuracy and operation stability. The existing vibration suppression control methods require high accuracy in dynamic modeling, but ignore factors such as hysteresis and creep of ropes, resulting in poor vibration suppression effect, and the complex modeling process increases the difficulty of calculation.

Method used

Using a vibration suppression control method based on mechanism model and data-driven vibration control method, modal analysis is performed through the reaction force device vibration excitation, natural frequency residual is obtained, deep learning modeling is trained, dynamic modeling results are compensated, and sliding mode controller is designed to minimize natural frequency vibration signal.

Benefits of technology

It realizes rapid vibration suppression at the end of the cable-parallel robot, improves operation accuracy and operation stability, avoids complex dynamic modeling processes, and improves integration.

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Abstract

The invention discloses a cable parallel robot vibration suppression control method based on a mechanism model and data driving, and the method comprises the steps: carrying out the dynamic modeling of a cable-driven parallel robot, and solving the inherent frequency of each point in a working space; randomly selecting points in the working space, moving the movable platform to a selected position, exciting by using a counter-acting force device, and recording pose changes by using a vibration sensor; according to the actual inherent frequency of the vibration data identification system, taking the spatial position-inherent frequency residual data set as input to train a deep learning model describing the relationship between the position coordinates and the inherent frequency residual; and a controller is designed with the minimum inherent frequency vibration signal as a design target to perform vibration suppression control, and the inherent frequency of each position is obtained by summation of residual errors given by a dynamic model predicted value and a deep learning model. Through the method, different cable parallel robots can be rapidly inhibited, and the operation precision and the working stability of the tail ends of the cable parallel robots are effectively improved.
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