{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T00:49:39Z","timestamp":1760230179386,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,7,14]],"date-time":"2022-07-14T00:00:00Z","timestamp":1657756800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Given the widespread use of the Kalman filter in robotics, an increasing number of researchers devote themselves to its study and application. This work underscores the importance of this filter while analyzing the modifications made to the same to improve its performance and reduce its deficiencies in some fields and presenting some of its applications in robotics. The following methods are presented in this study: least squares (LS), Hopfield Neural Networks (HNN), Extended Kalman filter (EKF), and Unscented Kalman filter (UKF). These methods are used in the parameter identification of a Selective Compliant Assembly Robot Arm (SCARA) robot with 3-Degrees of Freedom (3-DoF) and a clamp at its end. The dynamic model of this robot is obtained and employed to identify its parameters; then, the identification results are compared considering the difference between the obtained parameters and the real values of the robot parameters; in this comparison, the good results yielded by the LS and UKF method stand out. Subsequently, the obtained parameters through each method are validated by measuring different performance indexes\u2014during trajectory tracking\u2014such as: Residual Mean Square Error (RMSE), Integral of the Absolute Error (IAE), and the Integral of the Square Error (ISE). In this way, a comparison of the robot\u2019s performance is possible.<\/jats:p>","DOI":"10.3390\/sym14071446","type":"journal-article","created":{"date-parts":[[2022,7,15]],"date-time":"2022-07-15T01:57:11Z","timestamp":1657850231000},"page":"1446","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Evaluation of Parameter Identification of a Real Manipulator Robot"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7197-8928","authenticated-orcid":false,"given":"Claudio","family":"Urrea","sequence":"first","affiliation":[{"name":"Electrical Engineering Department, Faculty of Engineering, University of Santiago of Chile, Las Sophoras 165, Estaci\u00f3n Central, Santiago 9170124, Chile"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7395-5874","authenticated-orcid":false,"given":"Rayko","family":"Agramonte","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, Faculty of Engineering, University of Santiago of Chile, Las Sophoras 165, Estaci\u00f3n Central, Santiago 9170124, Chile"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1115\/1.3662552","article-title":"A New Approach to Linear Filtering and Prediction Problems","volume":"82","author":"Kalman","year":"1960","journal-title":"J. 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