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Adaptive fuzzy super-twisting sliding mode control with nonlinear active disturbance rejection for electromechanical stabilizer bars to improve vehicle roll stability.

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Τίτλος: Adaptive fuzzy super-twisting sliding mode control with nonlinear active disturbance rejection for electromechanical stabilizer bars to improve vehicle roll stability.
Συγγραφείς: Nguyen TA; Thuyloi University, Hanoi, Vietnam.
Πηγή: PloS one [PLoS One] 2026 Sep 28; Vol. 21 (9), pp. e0356645. Date of Electronic Publication: 2026 Sep 28 (Print Publication: 2026).
Τύπος έκδοσης: Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
Imprint Name(s): Original Publication: San Francisco, CA : Public Library of Science
Ιατρικοί όροι (MeSH): Fuzzy Logic* , Automobiles* , Motor Vehicles*, Models, Theoretical ; Nonlinear Dynamics ; Computer Simulation ; Soft Computing ; Algorithms
Περίληψη: Electromechanical stabilizer bars are commonly equipped in luxury vehicles to enhance rollover stability during aggressive steering maneuvers at high speeds. However, most existing studies simplify actuator and vehicle dynamics, while parametric uncertainties, model nonlinearities, and external disturbances are often insufficiently addressed. This paper proposes a robust adaptive control framework for an active anti-roll system with three main contributions. First, a comprehensive coupled dynamic model is established to explicitly capture the interaction between the electromechanical stabilizer-bar actuator and vehicle dynamics. Second, an integrated Super-Twisting Sliding Mode Control and Nonlinear Active Disturbance Rejection Control scheme is developed, in which the nonlinear extended state observer estimates and compensates for lumped disturbances and uncertainties, while the super-twisting mechanism provides robust motor current tracking. Third, an adaptive fuzzy gain-scheduling mechanism adjusts the control gain online according to vehicle operating conditions. The novelty therefore lies in the coordinated integration of coupled actuator-vehicle modeling, disturbance compensation, robust current tracking, and operating-condition-dependent gain adaptation within an electromechanical stabilizer-bar control framework. Numerical simulations demonstrate superior performance to the benchmark controllers in reducing vertical tire-force variations and improving vehicle dynamics. Under a J-turn maneuver at 100 km/h, the maximum motor-current tracking error is limited to 0.11 A, with an overshoot of 3.59% and a settling time of 0.17 s. These results demonstrate the effectiveness and potential of the proposed framework for automotive active control systems.
(Copyright: © 2026 Tuan Anh Nguyen. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Competing Interests: The authors have declared that no competing interests exist.
References: PLoS One. 2025 Apr 11;20(4):e0321664. (PMID: 40215223)
ISA Trans. 2021 Mar;109:259-268. (PMID: 33127080)
J Safety Res. 2022 Feb;80:391-398. (PMID: 35249620)
PLoS One. 2021 Aug 20;16(8):e0256301. (PMID: 34415932)
Int J Environ Res Public Health. 2022 Feb 11;19(4):. (PMID: 35206222)
PLoS One. 2023 Mar 7;18(3):e0282505. (PMID: 36881598)
PLoS One. 2023 Apr 13;18(4):e0284018. (PMID: 37053229)
Comput Biol Med. 2024 Feb;169:107957. (PMID: 38190767)
ISA Trans. 2022 Nov;130:152-162. (PMID: 35428479)
Entry Date(s): Date Created: 20260928 Date Completed: 20260928 Latest Revision: 20260930
Update Code: 20260930
PubMed Central ID: PMC13618959
DOI: 10.1371/journal.pone.0356645
PMID: 42804464
Βάση Δεδομένων: MEDLINE
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  Data: Adaptive fuzzy super-twisting sliding mode control with nonlinear active disturbance rejection for electromechanical stabilizer bars to improve vehicle roll stability.
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  Data: <searchLink fieldCode="AU" term="%22Nguyen+TA%22">Nguyen TA</searchLink>; Thuyloi University, Hanoi, Vietnam.
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  Data: Electromechanical stabilizer bars are commonly equipped in luxury vehicles to enhance rollover stability during aggressive steering maneuvers at high speeds. However, most existing studies simplify actuator and vehicle dynamics, while parametric uncertainties, model nonlinearities, and external disturbances are often insufficiently addressed. This paper proposes a robust adaptive control framework for an active anti-roll system with three main contributions. First, a comprehensive coupled dynamic model is established to explicitly capture the interaction between the electromechanical stabilizer-bar actuator and vehicle dynamics. Second, an integrated Super-Twisting Sliding Mode Control and Nonlinear Active Disturbance Rejection Control scheme is developed, in which the nonlinear extended state observer estimates and compensates for lumped disturbances and uncertainties, while the super-twisting mechanism provides robust motor current tracking. Third, an adaptive fuzzy gain-scheduling mechanism adjusts the control gain online according to vehicle operating conditions. The novelty therefore lies in the coordinated integration of coupled actuator-vehicle modeling, disturbance compensation, robust current tracking, and operating-condition-dependent gain adaptation within an electromechanical stabilizer-bar control framework. Numerical simulations demonstrate superior performance to the benchmark controllers in reducing vertical tire-force variations and improving vehicle dynamics. Under a J-turn maneuver at 100 km/h, the maximum motor-current tracking error is limited to 0.11 A, with an overshoot of 3.59% and a settling time of 0.17 s. These results demonstrate the effectiveness and potential of the proposed framework for automotive active control systems.<br /> (Copyright: © 2026 Tuan Anh Nguyen. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
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  Data: The authors have declared that no competing interests exist.
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  Data: PLoS One. 2025 Apr 11;20(4):e0321664. (PMID: <searchLink fieldCode="PM" term="%2240215223%22">40215223)</searchLink><br />ISA Trans. 2021 Mar;109:259-268. (PMID: <searchLink fieldCode="PM" term="%2233127080%22">33127080)</searchLink><br />J Safety Res. 2022 Feb;80:391-398. (PMID: <searchLink fieldCode="PM" term="%2235249620%22">35249620)</searchLink><br />PLoS One. 2021 Aug 20;16(8):e0256301. (PMID: <searchLink fieldCode="PM" term="%2234415932%22">34415932)</searchLink><br />Int J Environ Res Public Health. 2022 Feb 11;19(4):. (PMID: <searchLink fieldCode="PM" term="%2235206222%22">35206222)</searchLink><br />PLoS One. 2023 Mar 7;18(3):e0282505. (PMID: <searchLink fieldCode="PM" term="%2236881598%22">36881598)</searchLink><br />PLoS One. 2023 Apr 13;18(4):e0284018. (PMID: <searchLink fieldCode="PM" term="%2237053229%22">37053229)</searchLink><br />Comput Biol Med. 2024 Feb;169:107957. (PMID: <searchLink fieldCode="PM" term="%2238190767%22">38190767)</searchLink><br />ISA Trans. 2022 Nov;130:152-162. (PMID: <searchLink fieldCode="PM" term="%2235428479%22">35428479)</searchLink>
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