Development of a new method for automated selection of robotic mechanic-assembly technologies based on the technical and economic criteria

Kyrylovych, Valerii; Dimitrov, L.; Melnychuk, Petro; Bohdanets, Anatolii; Shostachuk, Andrii

doi:10.15587/1729-4061.2019.184294

Cited by 2 publications

(1 citation statement)

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“…As a result, the use of industrial robots has grown significantly in the context of industrial production in recent years, so investments in industrial robots continue to increase. Global sales of industrial robots doubled from 2013 to 2017 (114%) and the outlook for the coming years (2018)(2019)(2020)(2021) estimates that global sales will increase by an average of 14% per year [1].…”

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%