A Nonquadratic Algorithm Based on the Extended Recursive Least-Squares Algorithm

Amaral, Luís Fernando Coelho; Lopes, Marcus Vinicius; Barros, Allan Kardec

doi:10.1109/lsp.2018.2864609

Cited by 7 publications

(2 citation statements)

References 12 publications

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“…9 for the training and the test stage, respectively, where one can see that the KF combined with the RLS has superior results compared to the classical methods. In the experiments, the matrix (16) and the scalar (17) are determined in order to verify its influence in the identification procedure. By observing the generated curves in the training and testing steps (Fig.…”

Section: Rls-kf Resultsmentioning

confidence: 99%

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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: Rls-kf Resultsmentioning

confidence: 99%

“…The ERLS algorithm was proposed for solving the over-determined normal equations in the instrumental variable approaches. In [16] an algorithm was proposed based on a nonlinear function of the error, motivated by the ERLS algorithm. Simulations were performed on the problem of tracking a nonlinear Rayleigh fading multi-path channel.…”

Section: Introductionmentioning

confidence: 99%