Short Paper: Identification by Recursive Least Squares with RMO Applied to a Robotic Manipulator

Souza, Darielson A.; Batista, José Eduardo; Reis, Laurinda L. N. dos; Santana, Killdary Aguiar de; José, Ralph; Jose, Nacher; Antonio, B. S.; Araújo, Rui

doi:10.1109/ai4i46381.2019.00031

Cited by 1 publication

(1 citation statement)

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“…In the context of advances in robotics and improvements in industrial systems, new ideas are emerging to improve robotics systems increasingly, in addition to making them more economical. The improvements concern the optimization of the model by approximations using identification techniques and computational intelligence [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Identification by Recursive Least Squares With Kalman Filter (RLS-KF) Applied to a Robotic Manipulator

et al. 2021

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The field of robotics has grown a lot over the years due to the increasing necessity of industrial production and the search for quality of industrialized products. The identification of a system requires that the model output be as close as possible to the real one, in order to improve the control system. Some hybrid identification methods can improve model estimation through computational intelligence techniques, mainly improving the limitations of a given linear technique. This paper presents as a main contribution a hybrid algorithm for the identification of industrial robotic manipulators based on the recursive least square (RLS) method, which has its matrix of regressors and vector of parameters optimized via the Kalman filter (KF) method (RLS-KF). It is also possible to highlight other contributions, which are the identification of a robotic joint driven by a three-phase induction motor, the comparison of the RLS-KF algorithm with RLS and extended recursive least square (ERLS) and the generation of the transfer function by each method. The results are compared with the well-known recursive least squares and extended recursive least squares considering the criteria of adjustable coefficient of determination (R 2 a ) and computational cost. The RLS-KF showed better results compared to the other two algorithms (RLS and ERLS). All methods have generated their respective transfer functions.

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Section: Introductionmentioning

confidence: 99%