{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T19:36:12Z","timestamp":1781724972138,"version":"3.54.5"},"reference-count":27,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,3,13]],"date-time":"2023-03-13T00:00:00Z","timestamp":1678665600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurorobot."],"abstract":"<jats:p>The intervention time of stability control system is determined by stability judgment, which is the basis of vehicle stability control. According to the different working conditions of the vehicle, we construct the phase plane of the vehicle's sideslip angle and sideslip angular velocity, and establish the sample dataset of the stable region of the different phase planes. To reduce the complexity of phase plane stable region division and avoid large amount of data, we established the support vector regression (SVR) model, and realized the automatic regression of dynamic stable region. The testing of the test set shows that the model established in this paper has strong generalization ability. We designed a direct yaw-moment control (DYC) stability controller based on linear time-varying model predictive control (LTV-MPC). The influence of key factors such as centroid position and road adhesion coefficient on the stable region is analyzed through phase diagram. The effectiveness of the stability judgment and control algorithm is verified by simulation tests.<\/jats:p>","DOI":"10.3389\/fnbot.2023.1149201","type":"journal-article","created":{"date-parts":[[2023,3,13]],"date-time":"2023-03-13T04:35:00Z","timestamp":1678682100000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Research on control strategy of vehicle stability based on dynamic stable region regression analysis"],"prefix":"10.3389","volume":"17","author":[{"given":"Zhaoyong","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yihang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weijun","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zefan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haosen","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoqiang","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangqiang","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,3,13]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"31","DOI":"10.3389\/fnbot.2019.00031","article-title":"SVM-based classification of sEMG signals for upper-limb self-rehabilitation training","volume":"13","author":"Cai","year":"2019","journal-title":"Front. 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