{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T09:33:27Z","timestamp":1771666407359,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,3,27]],"date-time":"2024-03-27T00:00:00Z","timestamp":1711497600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["51975569"],"award-info":[{"award-number":["51975569"]}]},{"name":"National Natural Science Foundation of China","award":["51675520"],"award-info":[{"award-number":["51675520"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>It is urgent for automated electric transportation vehicles in coal mines to have the ability of self-adaptive tracking target constant deceleration to ensure stable and safe braking effects in long underground roadways. However, the current braking control system of underground electric trackless rubber-tired vehicles (UETRVs) still adopts multi-level constant braking torque control, which cannot achieve target deceleration closed-loop control. To overcome the disadvantages of lower safety and comfort, and the non-precise stopping distance, this article describes the architecture and working principle of constant deceleration braking systems with an electro-mechanical braking actuator. Then, a deceleration closed-loop control algorithm based on fuzzy neural network PID is proposed and simulated in Matlab\/Simulink. Finally, an actual brake control unit (BCU) is built and tested in a real industrial field setting. The test illustrates the feasibility of this constant deceleration control algorithm, which can achieve constant decelerations within a very short time and maintain a constant value of \u22122.5\u00a0m\/s2 within a deviation of \u00b10.1\u00a0m\/s2, compared with the deviation of 0.11\u00a0m\/s2 of fuzzy PID and the deviation of 0.13\u00a0m\/s2 of classic PID. This BCU can provide electric and automated mine vehicles with active and smooth deceleration performance, which improves the level of electrification and automation for mine transport machinery.<\/jats:p>","DOI":"10.3390\/s24072129","type":"journal-article","created":{"date-parts":[[2024,3,27]],"date-time":"2024-03-27T12:41:57Z","timestamp":1711543317000},"page":"2129","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Fuzzy Neural Network PID-Based Constant Deceleration Control for Automated Mine Electric Vehicles Using EMB System"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-3437-9070","authenticated-orcid":false,"given":"Jian","family":"Li","sequence":"first","affiliation":[{"name":"School of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China"}]},{"given":"Chi","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China"}]},{"given":"Yuqiang","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wu, M., Chen, C., Tian, C., and Zhou, J. 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