{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:02:02Z","timestamp":1760230922674,"version":"build-2065373602"},"reference-count":23,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,8,19]],"date-time":"2022-08-19T00:00:00Z","timestamp":1660867200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Strategic academic leadership program \u2018Priority 2030\u2019","award":["Agreement 075-15-2021-1333"],"award-info":[{"award-number":["Agreement 075-15-2021-1333"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotics"],"abstract":"<jats:p>The subject of this paper is a programmable con trol system for a robotic manipulator. Considering the complex nonlinear dynamics involved in practical applications of systems and robotic arms, the traditional control method is here replaced by the designed Elma and adaptive radial basis function neural network\u2014thereby improving the system stability and response rate. Related controllers and compensators were developed and trained using MATLAB-related software. The training results of the two neural network controllers for the robot programming trajectories are presented and the dynamic errors of the different types of neural network controllers and two control methods are analyzed.<\/jats:p>","DOI":"10.3390\/robotics11040083","type":"journal-article","created":{"date-parts":[[2022,8,22]],"date-time":"2022-08-22T01:56:40Z","timestamp":1661133400000},"page":"83","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Improving the Accuracy of a Robot by Using Neural Networks (Neural Compensators and Nonlinear Dynamics)"],"prefix":"10.3390","volume":"11","author":[{"given":"Zhengjie","family":"Yan","sequence":"first","affiliation":[{"name":"Institute of Computer Science and Technology, Peter the Great St. Petersburg Polytechnic University, Polytechnicheskaya 29, St. Petersburg 195251, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yury","family":"Klochkov","sequence":"additional","affiliation":[{"name":"Institute of Computer Science and Technology, Peter the Great St. Petersburg Polytechnic University, Polytechnicheskaya 29, St. Petersburg 195251, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin","family":"Xi","sequence":"additional","affiliation":[{"name":"Information Construction Management Office, MinZu University of China, No. 27 South Street, Zhongguancun, Haidian District, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.protcy.2013.12.451","article-title":"Neural Network based Inverse Kinematics Solution for Trajectory Tracking of a Robotic Arm","volume":"12","author":"Duka","year":"2014","journal-title":"Procedia Technol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Arseniev, D.G., Overmeyer, L., K\u00e4lvi\u00e4inen, H., and Katalini\u0107, B. 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