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Abstract



Robustness Analysis of Look-ahead Control for Heavy Trucks


This thesis work provides an approach to analyse the robustness of a fuel-time-optimal controller for the longitudinal dynamics of heavy trucks. The analysed look-ahead control system uses a predictive control strategy, developed in a collaboration of Scania and Linköpings Universitet. It utilizes GPS positioning data and a road slope database to compute the optimal fueling, braking and gear choice. In this study, the robustness towards parametric uncertainties is tested in various simulations on a Matlab/Simulink model. The experiment vehicle, which is modeled, is a Scania tractor with semitrailer of 15 to 40 tonnes with a 310Hp engine. The main focus within these tests is on uncertainties of the vehicle mass. The simulation model is based on the evaluation model from [Hellström et. al 2010], which was used to simulate the look-ahead control prior to and after practical experiments. The model has been modified to include perturbations of the vehicle mass and positioning data. The various simulations consist of a set of runs on a modeled 120km long motorway link between the two Swedish cities of Norrköping and Södertälje and the uncertainties are being analysed by changing one factor at a time in the simulation model. In conclusions, the analysed controller acts very robust towards uncertainties in mass, though the effects of a wrong estimated mass get bigger at lower masses. At 15t of true mass, an estimated mass over 19t would lead to a fuel-time consumption that is higher than the comparable cruise controller result. A true mass of 20t does already require an estimated mass over 40t to get insufficient results. Lower estimated masses result in the worst case in the same fuel-time use as the cruise controller. At higher true masses all tested estimated masses cause satisfactory fuel consumption and trip time. However, since the estimated mass is not very likely to exceed ±10% of the true mass, the controller should be practically robust in all tested scenarios. Uncertainties of the GPS position have been analysed in a large range and the results display the controller very robust towards these errors as well. The algorithm is even with bigger uncertianties capable of lowering the fuel consumtion without increasing the trip time.

Wolf Krahwinkel

2010

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