Robot joint motor parameter identification and control parameter self-tuning method

Abstract

The invention discloses a robot joint motor parameter identification and control parameter self-tuning method. A robot joint motor is controlled by adopting a permanent magnet synchronous motor vector control method, voltage, current and rotating speed of the joint motor on line are acquired, firstly, the resistance, inductance and flux linkage of the joint motor are preliminarily identified through a forgetting factor-based least square algorithm (RLS), then identification values serve as initial values of a model reference adaptive algorithm (MRAS), the parameters of the joint motor are further identified online, and the identification speed and precision are improved. And finally, based on the resistance and inductance identification values of the joint motor, online self-tuning is carried out on PID control parameters of a current loop of the joint motor, and high-performance control over the current of the joint motor of the robot is achieved. The method has the advantages of being high in identification speed and high in identification precision aiming at the robot joint motor parameters, rapid online self-tuning of the joint motor control parameters can be achieved, and the joint motor control performance is improved.

Description

technical field [0001] The invention relates to the field of motor control, in particular to a method for identifying motor parameters of robot joints and self-tuning control parameters. Background technique [0002] Robots are playing an increasingly important role in industrial production, life services, medical care, aerospace and other related fields. The control performance of the permanent magnet synchronous motor of the servo motor used in the robot joint plays a vital role in the motion control of the robot. During the working process of the permanent magnet synchronous motor, as the working time increases and the temperature rises, the parameters of the motor stator resistance and rotor flux linkage will change, and the motor stator inductance and rotor flux linkage will also be affected by the saturation of the magnetic circuit. In order to obtain more Excellent control performance requires accurate identification of motor parameters, so as to realize online self-...

AI Technical Summary

Problems solved by technology

The identification process of the least squares method is simple and the convergence speed is fast, but the identification accuracy is affected by noise interference and speed fluctuations

The model parameter adaptive method has the advantages of simple algorithm and easy implementation, but its convergence speed and accuracy are greatly affected by the initial value

Extended Kalman filter requires a large number of matrix and vector operations, and the calculation is very complicated when there are many identification parameters

Intelligent algorithms have problems such as large amount of data required in the early stage, high computational complexity, and high requirements for hardware.

Images