A vehicle stability control strategy with adaptive neural network sliding mode theory based on system uncertainty approximation

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dc.contributor.authorJi, Xuewu
dc.contributor.authorHe, Xiangkun
dc.contributor.authorLv, Chen
dc.contributor.authorLiu, Yahui
dc.contributor.authorWu, Jian
dc.date.accessioned2018-03-09T12:32:22Z
dc.date.available2018-03-09T12:32:22Z
dc.date.issued2017-11-20
dc.description.abstractModelling uncertainty, parameter variation and unknown external disturbance are the major concerns in the development of an advanced controller for vehicle stability at the limits of handling. Sliding mode control (SMC) method has proved to be robust against parameter variation and unknown external disturbance with satisfactory tracking performance. But modelling uncertainty, such as errors caused in model simplification, is inevitable in model-based controller design, resulting in lowered control quality. The adaptive radial basis function network (ARBFN) can effectively improve the control performance against large system uncertainty by learning to approximate arbitrary nonlinear functions and ensure the global asymptotic stability of the closed-loop system. In this paper, a novel vehicle dynamics stability control strategy is proposed using the adaptive radial basis function network sliding mode control (ARBFN-SMC) to learn system uncertainty and eliminate its adverse effects. This strategy adopts a hierarchical control structure which consists of reference model layer, yaw moment control layer, braking torque allocation layer and executive layer. Co-simulation using MATLAB/Simulink and AMESim is conducted on a verified 15-DOF nonlinear vehicle system model with the integrated-electro-hydraulic brake system (I-EHB) actuator in a Sine With Dwell manoeuvre. The simulation results show that ARBFN-SMC scheme exhibits superior stability and tracking performance in different running conditions compared with SMC scheme.en_UK
dc.identifier.citationJi X, He X, Lv C, Liu Y, Wu J, A vehicle stability control strategy with adaptive neural network sliding mode theory based on system uncertainty approximation, Vehicle System Dynamics, Volume 56, 2018, Issue 6, pp. 923-946en_UK
dc.identifier.issn0042-3114
dc.identifier.urihttp://dx.doi.org/10.1080/00423114.2017.1401100
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/13065
dc.language.isoenen_UK
dc.publisherTaylor & Francisen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectVehicle dynamics controlen_UK
dc.subjectSystem uncertaintyen_UK
dc.subjectAdaptive neural networken_UK
dc.subjectSliding mode controlen_UK
dc.subjectExtreme drving conditionen_UK
dc.titleA vehicle stability control strategy with adaptive neural network sliding mode theory based on system uncertainty approximationen_UK
dc.typeArticleen_UK

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