{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T22:45:15Z","timestamp":1776811515222,"version":"3.51.2"},"reference-count":20,"publisher":"European Society of Computational Methods in Sciences and Engineering","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"published-print":{"date-parts":[[2022,7,8]]},"abstract":"<jats:p>The current speed control methods of mineral hoisting and conveying machinery ignore the fuzzy steps of the PID control model, and the AC commutation effect of mineral hoisting and conveying machinery is poor, resulting in long delay and low accuracy of speed control. To this end, a speed control method for lifting and transporting machinery based on single neuron PID is proposed. The structure of a single neural adaptive PID speed controller is designed. A learning algorithm is introduced to optimize the AC commutation effect of the hoisting and conveying mechanical circuit. A single neuron PID controller is used to fuzzy process the input signal to form a fuzzy language set. The speed controller port is defined, and the single neuron PID control rule is designed. Using the reduction ratio of the three-phase motor speed governor, the maximum speed required for conveying minerals is calculated, and the speed control of the mineral hoisting and transporting machinery is realized. The experimental results show that the mineral lifting deviation is small, the speed regulation time is less than 4 s, the average control delay is 200 ms, and the maximum speed regulation accuracy is 99.9%. The method in this paper has certain application value and is worthy of promotion.<\/jats:p>","DOI":"10.3233\/jcm226044","type":"journal-article","created":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T13:27:24Z","timestamp":1648560444000},"page":"1263-1275","source":"Crossref","is-referenced-by-count":1,"title":["Speed control method of mineral lifting and transportation machinery based on single neuron PID"],"prefix":"10.66113","volume":"22","author":[{"given":"Hongtao","family":"Jiao","sequence":"first","affiliation":[{"name":"The College Office, Zhengzhou Railway Vocational and Technical College, Zhengzhou, Henan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baoli","family":"Wei","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Electrical Engineering, Zhengzhou Railway Vocational and Technical College, Zhengzhou, Henan, 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