Implementation of Active-anti Roll Bar Control System to a 3-axle Double-Decker Bus

Loo, Tze Peng (2019) Implementation of Active-anti Roll Bar Control System to a 3-axle Double-Decker Bus. Final Year Project (Bachelor), Tunku Abdul Rahman University College.

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Abstract

Double-decker buses have a large amount of seating capacity and it has potential to reduce air pollution and traffic on the road. However, a fully seated double-decker bus will have an enormously high centre of gravity, which will cause roll angle and roll rate of the vehicle to be huge and lead to discomfort. Moreover, their roll stability will easily loss with a moderate level of lateral acceleration. In this project, the existing control system of active anti-roll bar will be tuned, implemented and modified to specific for double-decker buses. A combination of mathematical modelling of ride comfort model (9 degrees of freedom), handling model (11 degrees of freedom) and varies of forces will be considered and simulate within Matlab/Simulink with the use of a self-tuning fuzzy logic PID controller to control continuous variable damping suspension system to obtain the results. Half step input test and zero input test will be use to validate the model, a scenarios will be test for ride comfort model that is half bump test, and followed by three scenarios will be considered for combination of ride comfort model and handling model. The scenarios are bus roll stability during a double lane change maneuver (ISO3888-1), 180 degrees curve road in highway and evades a sudden appear obstacle (moose test ISO3888-2). For all the tests, main priority of performance improvement will be focus on roll angle, roll rate, comfort due to bounce and lateral accelerations. Overall, all test results shows promising improvement in performance Throughout whole tests, on average, active anti-roll bar is able to improve the performance up to at least 90%, however lateral acceleration will be scarified by about 2.22% on average. Whereas passive anti-roll bar configurations are worse than that.