CN-121973201-A - Intelligent leveling control method and system for flexible mechanical arm

CN121973201ACN 121973201 ACN121973201 ACN 121973201ACN-121973201-A

Abstract

The invention discloses an intelligent leveling control method and system for a flexible mechanical arm, which relate to the technical field of mechanical intelligent control and comprise the steps of collecting data of the flexible mechanical arm in a static state and an inclined state, constructing a gravity field inclined reverse mapping model based on the collected data, adopting an FFT frequency domain analysis method to extract disturbance signal characteristics, combining the gravity field inclined reverse mapping model to generate a leveling control instruction, using a controller to send the leveling control instruction to a driving flexible actuator to perform leveling operation and perform closed-loop correction based on operation feedback, constructing the gravity field inclined reverse mapping model, utilizing FFT frequency domain analysis to extract disturbance characteristics and generate the leveling control instruction, and carrying out self-adaptive adjustment on the flexible actuator by combining a closed-loop feedback mechanism, so that the problems of low leveling control precision and poor robustness of the flexible mechanical arm are effectively solved.

Inventors

CHEN MINGYUAN
LIU YOUPING
XIA CHONGKUN
WU WENBIN
YANG JINGRUI

Assignees

中山大学附属第五医院
中山大学

Dates

Publication Date: 20260505
Application Date: 20260126

Claims (10)

1. An intelligent leveling control method for a flexible mechanical arm is characterized by comprising the following steps of, Collecting data of the flexible mechanical arm in static and inclined states, and constructing a gravity field inclination reverse mapping model based on the collected data; Extracting disturbance signal characteristics by adopting an FFT frequency domain analysis method, and generating a leveling control instruction by combining a gravity field inclination reverse mapping model; and sending a leveling control instruction to a driving flexible actuator by using a controller to perform leveling operation, and performing closed-loop correction based on operation feedback.
2. The intelligent leveling control method of the flexible mechanical arm according to claim 1, wherein the step of acquiring data of the flexible mechanical arm in a static state and an inclined state is to deploy an IMU sensor and a flexible position coding sensor at two ends and joint parts of the flexible mechanical arm, acquire triaxial acceleration, triaxial angular velocity and position coding information, and output a data set of each moment of the mechanical arm Each data in the dataset includes tri-axial acceleration Angular velocity of three axes And angle data ; And uploading the data set of the mechanical arm at each moment to a database for storage.
3. The intelligent leveling control method of the flexible mechanical arm according to claim 2, wherein the constructing the gravity field tilting reverse mapping model based on the collected data means calculating the acceleration variation of the mechanical arm in a tilting posture ; Under the inclined posture, calculating the inclined angle of the node M at the moment t by adopting an included angle formula according to the acceleration variation and the static reference acceleration Cosine value of (2) ; Taking the inverse cosine to obtain the inclination angle of the mechanical arm ; Based on the amount of change in acceleration Angle of inclination with mechanical arm Obtaining a gravitational field inclination reverse mapping model 。
4. The intelligent leveling control method of the flexible mechanical arm of claim 3, wherein the extracting of disturbance signal features by the FFT frequency domain analysis method means performing Fourier transform on the acceleration variation of each axis to obtain a complex spectrum of the acceleration variation of the ith axis of the node M in the frequency domain at the time t ; Calculating a power spectrum of an ith axis of an M node using complex spectra ; Extracting the frequency corresponding to the maximum value in the power spectrum as the main disturbance frequency ; Extracting disturbance characteristics of M node multiaxial frequency domain The primary disturbance frequency at M node x Main disturbance frequency of y-axis And the principal disturbance frequency of the z-axis ; Finally outputting disturbance characteristics of multi-axis frequency domain For generating leveling control instructions.
5. The intelligent leveling control method of the flexible mechanical arm of claim 4, wherein the generation of the leveling control command by combining the gravity field inclination reverse mapping model is to construct direction sensing weights through disturbance characteristics of a multi-axis frequency domain Based on perceptual weights Generating uniaxial directional control commands By means of Obtaining control instruction vectors Final output of Is a leveling control instruction.
6. The intelligent leveling control method of the flexible mechanical arm is characterized in that the controller is used for sending a leveling control instruction to the flexible actuator to drive the flexible actuator to perform leveling operation, the controller is used for analyzing multi-axis target deformation of each node in the leveling control instruction, the actuator is placed into an initial state, the controller is used for sending the analyzed instruction to the flexible actuator, and the actuator levels the mechanical arm according to the instruction.
7. The intelligent leveling control method of the flexible mechanical arm according to claim 6, wherein the closed loop correction based on the operation feedback is to collect angle data of each node of the leveled mechanical arm The collected angle data and the angle data of the standard leveling state of the mechanical arm are processed Comparing to obtain angle difference Setting an angle difference threshold : If it is Greater than or equal to Regenerating a leveling instruction; If it is Less than And uploading the adjustment experience generated in the leveling process to a database.
8. An intelligent leveling control system of a flexible mechanical arm is based on the intelligent leveling control method of the flexible mechanical arm of any one of claims 1-7, and is characterized by comprising, The data acquisition module is used for acquiring data of the mechanical arm in a static state and an inclined state; the modeling module is used for constructing a gravity field inclination reverse mapping model by using the acquired data; the signal extraction module is used for carrying out frequency domain analysis on the mechanical arm by using FFT and extracting disturbance characteristics; the instruction generation module is used for generating a leveling instruction based on the model and the disturbance characteristics; The execution control module is used for controlling the flexible executor to respond to the leveling instruction; And the closed loop correction module is used for carrying out error detection and correction based on the operation feedback.
9. A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the computer device is characterized in that the processor realizes the steps of the intelligent leveling control method of the flexible mechanical arm according to any one of claims 1-7 when executing the computer program.
10. A computer readable storage medium, on which a computer program is stored, is characterized in that the computer program, when being executed by a processor, implements the steps of a flexible mechanical arm intelligent leveling control method according to any one of claims 1 to 7.

Description

Intelligent leveling control method and system for flexible mechanical arm Technical Field The invention relates to the technical field of mechanical intelligent control, in particular to an intelligent leveling control method and system for a flexible mechanical arm. Background With the continuous progress of robot technology, flexible mechanical arms are widely used in a plurality of fields such as service robots, medical assistance, flexible manufacturing and the like due to high flexibility, light weight structure and good safety. Compared with the traditional rigid mechanical arm, the flexible mechanical arm has remarkable advantages in the tasks of cooperation with people, coping with complex environment interference, completing flexible grabbing and the like. However, because of the great elasticity of the body structure and the complex attitude control, the space attitude adjustment and dynamic stability control become one of the key problems in the current research and engineering application. Especially in a dynamic scene or a non-ideal working environment, factors such as external disturbance, unbalanced gravity and the like are easy to cause the posture offset of the mechanical arm, so that the overall control precision and response stability of the system are affected. In recent years, related researches try to introduce various sensors to realize state feedback, construct a motion model by using a physical modeling mode, or perform adjustment control by a learning algorithm, and although certain effects are achieved, a large improvement space still exists in the aspects of instantaneity, self-adaptive capacity and control closed-loop precision. In the prior art, the problem of leveling the posture of the flexible mechanical arm is classified into direct calculation and error correction of position and angle sensor data, but the method is difficult to accurately restore the real inclined state of the flexible arm in the gravity field under the uncertain disturbance condition, and on the other hand, the existing signal processing and control strategies, such as time domain filtering, PID control and other methods, respond slowly when facing complex frequency disturbance or coupling nonlinear behaviors, and leveling instructions have hysteresis problems to influence the adjustment efficiency and accuracy. In addition, the current closed-loop feedback system has multiple stress error compensation layers and lacks a control strategy optimization mechanism starting from disturbance source characteristics. Aiming at the problems, an intelligent leveling method capable of integrating gesture modeling, disturbance recognition and control decision is needed to improve stability, autonomy and leveling control performance of the flexible mechanical arm in a non-ideal environment. Disclosure of Invention The present invention has been made in view of the above-described problems occurring in the prior art. Therefore, the invention provides an intelligent leveling control method for a flexible mechanical arm, which solves the problems of posture leveling precision and control response lag of the flexible mechanical arm under the unbalanced conditions of dynamic disturbance and gravity. In order to solve the technical problems, the invention provides the following technical scheme: In a first aspect, the invention provides an intelligent leveling control method for a flexible mechanical arm, which comprises the steps of collecting data of the flexible mechanical arm in a static state and an inclined state, and constructing a gravity field inclination reverse mapping model based on the collected data; Extracting disturbance signal characteristics by adopting an FFT frequency domain analysis method, and generating a leveling control instruction by combining a gravity field inclination reverse mapping model; and sending a leveling control instruction to a driving flexible actuator by using a controller to perform leveling operation, and performing closed-loop correction based on operation feedback. The invention relates to an intelligent leveling control method for a flexible mechanical arm, which comprises the following steps of acquiring data of the flexible mechanical arm in a static state and an inclined state, deploying an IMU sensor and a flexible position coding sensor at two ends and joint positions of the flexible mechanical arm, acquiring triaxial acceleration, triaxial angular velocity and position coding information, and outputting a data set of each moment of the mechanical armEach data in the dataset includes tri-axial accelerationAngular velocity of three axesAnd angle data; And uploading the data set of the mechanical arm at each moment to a database for storage. As a preferable scheme of the intelligent leveling control method of the flexible mechanical arm, the invention comprises the steps that the construction of the gravity field inclination reverse mapping model based on the acquired data