{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:41:17Z","timestamp":1775068877181,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T00:00:00Z","timestamp":1725926400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Free University of Bozen-Bolzano"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Brain-computer interface (BCI) provides direct communication and control between the human brain and physical devices. It is achieved by converting EEG signals into control commands. Such interfaces have significantly improved the lives of disabled individuals suffering from neurological disorders\u2014such as stroke, amyotrophic lateral sclerosis (ALS), and spinal cord injury\u2014by extending their movement range and thereby promoting self-independence. Brain-controlled mobile robots, however, often face challenges in safety and control performance due to the inherent limitations of BCIs. This paper proposes a shared control scheme for brain-controlled mobile robots by utilizing fuzzy logic to enhance safety, control performance, and robustness. The proposed scheme is developed by combining a self-learning neuro-fuzzy (SLNF) controller with an obstacle avoidance controller (OAC). The SLNF controller robustly tracks the user\u2019s intentions, and the OAC ensures the safety of the mobile robot following the BCI commands. Furthermore, SLNF is a model-free controller that can learn as well as update its parameters online, diminishing the effect of disturbances. The experimental results prove the efficacy and robustness of the proposed SLNF controller including a higher task completion rate of 94.29% (compared to 79.29%, and 92.86% for Direct BCI and Fuzzy-PID, respectively), a shorter average task completion time of 85.31 s (compared to 92.01 s and 86.16 s for Direct BCI and Fuzzy-PID, respectively), and reduced settling time and overshoot.<\/jats:p>","DOI":"10.3390\/s24185875","type":"journal-article","created":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T09:10:57Z","timestamp":1725959457000},"page":"5875","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Intelligent Control System for Brain-Controlled Mobile Robot Using Self-Learning Neuro-Fuzzy Approach"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7171-003X","authenticated-orcid":false,"given":"Zahid","family":"Razzaq","sequence":"first","affiliation":[{"name":"Faculty of Engineering, Free University of Bozen-Bolzano, 39100 Bozen-Bolzano, Italy"},{"name":"Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genoa, 16126 Genova, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5987-9919","authenticated-orcid":false,"given":"Nihad","family":"Brahimi","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1623-5317","authenticated-orcid":false,"given":"Hafiz Zia Ur","family":"Rehman","sequence":"additional","affiliation":[{"name":"Department of Mechatronics Engineering, Air University, Islamabad 44000, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2002-8951","authenticated-orcid":false,"given":"Zeashan Hameed","family":"Khan","sequence":"additional","affiliation":[{"name":"Interdisciplinary Research Center for Intelligent Manufacturing and Robotics (IRC-IMR), King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1109\/TSMCC.2012.2219046","article-title":"EEG-based brain-controlled mobile robots: A survey","volume":"43","author":"Bi","year":"2013","journal-title":"IEEE Trans. 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