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Anti-jerk controller with optimisation-based self-tuning.

Abuasaker, Sufyan (2016) Anti-jerk controller with optimisation-based self-tuning. Doctoral thesis, University of Surrey.

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Abstract

One of the major phenomena compromising the comfort of the passenger vehicles is jerk. Jerk occurs as a response to the transient in the driver torque demand. The transient provokes torsional oscillation of the drivetrain, which results in oscillations and jerk of the vehicle. These oscillations and jerk are transmitted to the driver and can cause discomfort to the driver and thus affecting the drivability of the vehicle. The aim of this work is to develop an anti-jerk controller to achieve smooth response of the vehicle and enhance the drivability metrics. The drivability analysis in this thesis focused on the longitudinal dynamic response during the tip-in manoeuvre. The anti-jerk controller introduced in this work is an optimisation-based controller. It is developed by using two models, i.e. a linear model and non-linear model. The developed models include detailed description of the drivetrain system such as clutch, primary shaft, secondary shaft, differential, half-shaft, tyres and the vehicle. The engine was modelled using the engine map. To achieve high confidence of the models fidelity, the models were verified by experimental data which ensures that the models are accurate and characterised by the required details. The anti-jerk controller is an optimised controller and uses a gain scheduling where the gain scheduling optimisation was performed off-line to reduce the engineering time in the controller gain tuning. The simulation results of the models with the controller show a significant improvement of the drivability, which is measured by the overshoot and the rise time on the acceleration profile.

Item Type: Thesis (Doctoral)
Subjects : Modelling, Controller, Optimisation
Divisions : Theses
Authors :
AuthorsEmailORCID
Abuasaker, Sufyans.abuasaker@surrey.ac.uksa00176
Date : 29 April 2016
Funders : -
Contributors :
ContributionNameEmailORCID
Thesis supervisorSorniotti, Aldoa.sorniotti@surrey.ac.ukas0025
Depositing User : Sufyan Abuasaker
Date Deposited : 13 May 2016 09:45
Last Modified : 13 May 2016 09:45
URI: http://epubs.surrey.ac.uk/id/eprint/810442

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