An efficient data acquisition methodology for inverse dynamics model learning of manipulator based on analytical method. II
Čelâbinskij fiziko-matematičeskij žurnal, Tome 8 (2023) no. 1, pp. 146-151.

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We consider a parametric physical model of a manipulator obtained from the rigid body dynamics using the analytical method. Our framework consists of the Denavit — Hartenberg method for the generation of manipulator workspace, the cubic polynomial method for the trajectory generation between two points, the Levenberg — Marquardt method for finding the required joint positions to reach the goal points and the Newton — Euler method for finding the required torque to execute the desired trajectory. The received datasets are validated by the results of simulation of kinematic and dynamic modeling of the tested manipulator.
Keywords: manipulator, model learning, Levenberg — Marquardt method, Newton — Euler method.
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R. Tu. An efficient data acquisition methodology for inverse dynamics model learning of manipulator based on analytical method. II. Čelâbinskij fiziko-matematičeskij žurnal, Tome 8 (2023) no. 1, pp. 146-151. http://geodesic.mathdoc.fr/item/CHFMJ_2023_8_1_a13/

[1] Burdet E., Codourey A., “Evaluation of parametric and nonparametric nonlinear adaptive controllers”, Robotica, 16:1 (1998), 59–73

[2] Nguyen-Tuong D., Peters J., “Model learning for robot control: A survey”, Cognitive Processing, 12:4 (2011), 319–340

[3] Strutz T., Data Fitting and Uncertainty (A practical introduction to weighted least squares and beyond), Springer, 2016

[4] Thu Rain, Dovgal V.M., Yan Naing Soe, “Modelling of the adaptive neuro-fuzzy inference system based control of 5-dof robotic manipulator “Intelbot””, Belgorod State University Scientific Bulletin. (Economics. Information Technologies), 45:3 (2018), 497–509 (In Russ.)

[5] Thu Rain, Yan Naing Soe, “Dynamic modelling of manipulator using adaptive neuro fuzzy inference system”, Modeling, Optimization and Information Technology (MOIT), 7:4 (2019), 362–377 (In Russ.)