{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T20:20:50Z","timestamp":1773778850878,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,30]],"date-time":"2022-12-30T00:00:00Z","timestamp":1672358400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51505414"],"award-info":[{"award-number":["51505414"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["ZD2019063"],"award-info":[{"award-number":["ZD2019063"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Universities of Hebei Province Education Department","award":["51505414"],"award-info":[{"award-number":["51505414"]}]},{"name":"Universities of Hebei Province Education Department","award":["ZD2019063"],"award-info":[{"award-number":["ZD2019063"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In order to improve the tracking adaptability of autonomous vehicles under different vehicle speeds and road curvature, this paper develops a weight adaptive model prediction control system (AMPC) based on PSO-BP neural network, which consists of a dynamics-based model prediction controller (MPC) and an optimal weight adaptive regulator. Based on the application of MPC to achieve high-precision tracking control, the optimal weight under different operating conditions obtained by automated simulation is used to train the PSO-BP neural network offline to achieve online adjustment of MPC weight. The validation results of the Prescan-Carsim-Simulink joint simulation platform show that the adaptive control system has better tracking adaptation capability compared with the original classical MPC control. The control strategy was also verified on an autonomous vehicle test platform, and the test results showed that the adaptive control strategy improved tracking accuracy while meeting the vehicle\u2019s requirements for real-time control and lateral stability.<\/jats:p>","DOI":"10.3390\/s23010412","type":"journal-article","created":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T03:08:59Z","timestamp":1672628939000},"page":"412","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Weight Adaptive Path Tracking Control for Autonomous Vehicles Based on PSO-BP Neural Network"],"prefix":"10.3390","volume":"23","author":[{"given":"Xianzhi","family":"Tang","sequence":"first","affiliation":[{"name":"Hebei Key Laboratory of Special Delivery Equipment, School of Vehicles and Energy, Yanshan University, Qinhuangdao 066004, China"}]},{"given":"Longfei","family":"Shi","sequence":"additional","affiliation":[{"name":"Hebei Key Laboratory of Special Delivery Equipment, School of Vehicles and Energy, Yanshan University, Qinhuangdao 066004, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1530-080X","authenticated-orcid":false,"given":"Bo","family":"Wang","sequence":"additional","affiliation":[{"name":"Hebei Key Laboratory of Special Delivery Equipment, School of Vehicles and Energy, Yanshan University, Qinhuangdao 066004, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7090-6575","authenticated-orcid":false,"given":"Anqi","family":"Cheng","sequence":"additional","affiliation":[{"name":"Hebei Key Laboratory of Special Delivery Equipment, School of Vehicles and Energy, Yanshan University, Qinhuangdao 066004, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1109\/MVT.2021.3086432","article-title":"Virtual Target-Based Longitudinal Motion Planning of Autonomous Vehicles at Urban Intersections: Determining Control Inputs of Acceleration With Human Driving Characteristic-Based Constraints","volume":"16","author":"Yoo","year":"2021","journal-title":"IEEE Veh. 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