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dc.contributor.authorBraghieri, Giovanni
dc.date.accessioned2018-05-03T14:12:59Z
dc.date.available2018-05-03T14:12:59Z
dc.date.issued2018-07-30
dc.date.submitted2017-09-25
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/275524
dc.description.abstractThe work undertaken in this research aims to develop a mathematical model which can replicate the behaviour of a racing driver controlling a vehicle at its handling limit. Most of the models proposed in the literature assume a perfect driver. A formulation taking human limitations into account would serve as a design and simulation tool for the automotive sector. A nonlinear vehicle model with five degrees of freedom under the action of external disturbances controlled by a Linear Quadratic Regulator (LQR) is first proposed to assess the validity of state variances as stability metrics. Comparison to existing stability and controllability criteria indicates that this novel metric can provide meaningful insights into vehicle performance. The LQR however, fails to stabilise the vehicle as tyres saturate. The formulation is extended to improve its robustness. Full nonlinear optimisation with direct transcription is used to derive a controller that can stabilise a vehicle at the handling limit under the action of disturbances. The careful choice of discretisation method and track description allow for reduced computing times. The performance of the controller is assessed using two vehicle configurations, Understeered and Oversteered, in scenarios characterised by increasing levels of non- linearity and geometrical complexity. All tests confirm that vehicles can be stabilised at the handling limit. Parameter studies are also carried out to reveal key aspects of the driving strategy. The driver model is validated against Driver In The Loop simulations for simple and complex manoeuvres. The analysis of experimental data led to the proposal of a novel driving strategy. Driver randomness is modelled as an external disturbance in the driver Neuromuscular System. The statistics of states and controls are found to be in good agreement. The prediction capabilities of the controller can be considered satisfactory.
dc.language.isoen
dc.rightsAll rights reserved
dc.rightsAll Rights Reserveden
dc.rights.urihttps://www.rioxx.net/licenses/all-rights-reserved/en
dc.subjectdriver modelling
dc.subjectvehicle dynamics
dc.subjectMPC control
dc.titleApplication of robust nonlinear model predictive control to simulating the control behaviour of a racing driver
dc.typeThesis
dc.type.qualificationlevelDoctoral
dc.type.qualificationnameDoctor of Philosophy (PhD)
dc.publisher.institutionUniversity of Cambridge
dc.publisher.departmentEngineering
dc.date.updated2018-05-03T13:46:45Z
dc.identifier.doi10.17863/CAM.22761
dc.publisher.collegeKing's College
dc.type.qualificationtitlePhD in Engineering
cam.supervisorCole, David
cam.thesis.fundingtrue
rioxxterms.freetoread.startdate2019-05-03


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