Predictive path-tracking control of an autonomous electric vehicle with various multi-actuation topologies

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dc.contributor.authorLin, Chenhui
dc.contributor.authorLi, Boyuan
dc.contributor.authorSiampis, Efstathios
dc.contributor.authorLongo, Stefano
dc.contributor.authorVelenis, Efstathios
dc.date.accessioned2024-03-18T15:45:19Z
dc.date.available2024-03-18T15:45:19Z
dc.date.issued2024-02-28
dc.description.abstractThis paper presents the development of path-tracking control strategies for an over-actuated autonomous electric vehicle. The vehicle platform is equipped with four-wheel steering (4WS) as well as torque vectoring (TV) capabilities, which enable the control of vehicle dynamics to be enhanced. A nonlinear model predictive controller is proposed taking into account the nonlinearities in vehicle dynamics at the limits of handling as well as the crucial actuator constraints. Controllers with different actuation formulations are presented and compared to study the path-tracking performance of the vehicle with different levels of actuation. The controllers are implemented in a high-fidelity simulation environment considering scenarios of vehicle handling limits. According to the simulation results, the vehicle achieves the best overall path-tracking performance with combined 4WS and TV, which illustrates that the over-actuation topology can enhance the path-tracking performance during conditions under the limits of handling. In addition, the performance of the over-actuation controller is further assessed with different sampling times as well as prediction horizons in order to investigate the effect of such parameters on the control performance, and its capability for real-time execution. In the end, the over-actuation control strategy is implemented on a target machine for real-time validation. The control formulation proposed in this paper is proven to be compatible with different levels of actuation, and it is also demonstrated in this work that it is possible to include the particular over-actuation formulation and specific nonlinear vehicle dynamics in real-time operation, with the sampling time and prediction time providing a compromise between path-tracking performance and computational time.en_UK
dc.identifier.citationLin C, Li B, Siampis E, et al., (2024) Predictive path-tracking control of an autonomous electric vehicle with various multi-actuation topologies. Sensors, Volume 24, Issue 5, February 2024, Article number 1566en_UK
dc.identifier.issn1424-8220
dc.identifier.urihttps://doi.org/10.3390/s24051566
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/21028
dc.language.isoen_UKen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectautonomous vehicleen_UK
dc.subjectpath trackingen_UK
dc.subjectmulti-actuationen_UK
dc.subjectpredictive controlen_UK
dc.titlePredictive path-tracking control of an autonomous electric vehicle with various multi-actuation topologiesen_UK
dc.typeArticleen_UK
dcterms.dateAccepted2024-02-26

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