Open AccessModeling and adaptive torque computed control of industrial robot based on lie algebra
Author(s) -
Zaiwu Mei,
Liping Chen,
Jun Ding
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1780/1/012029
Subject(s) - control theory (sociology) , torque , industrial robot , robotic arm , control engineering , adaptive control , computer science , robot , direct torque control , inverse , damping torque , engineering , mathematics , control (management) , artificial intelligence , physics , geometry , electrical engineering , voltage , thermodynamics , induction motor
Computed Torque Control (CTC) is the most direct and effective way to improve the motion control performance of robot. But the computation of the joint torque is quite difficult, and because of the uncertainty of the parameters, an accurate inverse robot dynamic model for torque generation is difficult to obtain. An efficient inverse dynamic model of the industrial robot based on lie algebra is proposed and applied to the computed torque control. In order to overcome the uncertainty of parameters, the inverse robot dynamic model is linearized and an adaptive computed torque control is proposed. In order to validate the adaptive torque computed control method, a multi-domain integrated system model of 6-DOF industrial robot is established and the simulation results show that the adaptive computed torque control system has the function of parameter self-learning, the inaccurate parameters converge to the true value finally. The adaptive control shows better control performance than the traditional computed torque control.