Abstract
Robot dynamics and its parameter identification are of great significance to the realization of optimal control and human–machine interaction. The objective of this research is to address the shortcomings of establishing and identifying the self-developed six-degree-of-freedom (6-DoF) collaborative robot dynamics, which leads to a large error in the predicted torque of the proposed robot. A long short-term memory (LSTM) in an optimized recurrent neural network (RNN) is proposed to compensate the dynamic model of the proposed 6-DoF collaborative robot based on the consideration of gravity, Coriolis force, inertial force, and friction force. The analysis and experimental findings provide promising results. The compensated collaborative robot dynamic model based on LSTM in an optimized RNN displays a good prediction on the actual torque, and the root-mean-square (RMS) error between predicted and actual torques are reduced by 61.8% to 78.9% compared to the traditional dynamic model. Results of the experimental applications demonstrate the validity of the proposed systematic error compensation strategy.
Highlights
An accurate robot dynamic model can be used as the basis for building simulation models, verifying advanced control algorithms, and performing in-depth analysis of control systems [1]
Limited to this research process, this paper only focuses upon the dynamic parameters of the typical last three joints of the collaborative robot
The proposed self-developed 6-DOF collaborative robot in this paper is provided with a light structure, small inertia force, and gravity
Summary
An accurate robot dynamic model can be used as the basis for building simulation models, verifying advanced control algorithms, and performing in-depth analysis of control systems [1]. With the rising requirements of dynamic performance and the concept of collaborative robots, conventional model-free control methods, such as the proportion integral differential (PID) control, exhibit low trajectory tracking accuracy and poor anti-disturbance capabilities, and present difficulty in implementing human–robot interaction tasks, such as collision detection and compliance control [2]. For dynamic models of multi-axis serial chain robots, gravity, Coriolis force, inertial force and friction are generally considered. The reducer flexibility, inertia force of motor rotors, etc
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