Abstract
This paper presents an identification method for robotic manipulators. It demonstrates how a dynamic model can be constructed with the help of the modified Newton–Euler formula. To model the friction of the joints, static friction modelling is used, in which the friction behaviour depends only on the actual velocity of the given joint. With these techniques, the model can be converted into a linear-in-parameters form, which can make the identification process easier. Two estimators are introduced to solve the identification problem, the least-squares and the weighted least-squares estimators, and the determination of the independently identifiable parameter vector to make the regression matrix maximal column rank is presented. The Frobenius norm is used as the condition of the regression matrix to optimise the excitation trajectories, and the form of the trajectories has been selected from the finite Fourier series. The method is tested in a simulated environment to achieve a three-degrees-of-freedom manipulator.
Highlights
Automation is playing an increasingly important role in industry, where manipulators are used to solve various types of problems
There are plenty of existing friction models that can be used in the identification of the dynamic model, but there has to be a trade-off between model accuracy and computational complexity
The implementation of the identification process was performed in MATLAB, while the dynamics and the friction modelling were evaluated in MATLAB Simulink (Section 3)
Summary
Automation is playing an increasingly important role in industry, where manipulators are used to solve various types of problems. In [10], the dynamics of the payload were determined by an identification process that measured actuator currents In this method, the dynamic parameters of the manipulator were known. The present study compares two methods to estimate the parameters of a robot dynamic model with static friction in each joint. It demonstrates how the reduced row echelon form of the regression matrix can be used to find the independently identifiable variables. The results in [9] are extended in this paper by presenting a method incorporating the friction model into the identification process.
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