This paper discusses an optimal linear quadratic control algorithm to improve the roll stability of a tractor semi-trailer using active semi-trailer steering. The controller minimises a combination of the path-tracking deviation of the trailer rear end relative to the path of the hitch point (5th wheel) and the lateral acceleration of trailer centre of gravity (CoG). First a linear vehicle model of tractor semi-trailer is constructed. Then a 'virtual driver' model for trailer steering control is introduced to minimise the path-tracking deviation of trailer rear end. The lateral acceleration of trailer CoG is included as a second objective of the optimal controller so as to improve roll stability. A Kalman filter with linear vehicle model is used to estimate unknown vehicle states, needed by the controller. Simulation results show that optimal control of semi-trailer steering could improve the roll stability significantly during transient manoeuvres while keeping the path-tracking deviation of trailer rear end within an acceptable range.

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A high-speed path-following controller for long combination vehicles (LCVs) was designed and implemented on a test vehicle consisting of a rigid truck towing a dolly and a semitrailer. The vehicle was driven through a 3.5 m wide lane change maneuver at 80 km/h. The axles of the dolly and trailer were steered actively by electrically-controlled hydraulic actuators. Substantial performance benefits were recorded compared with the unsteered vehicle. For the best controller weightings, performance improvements relative to unsteered case were: lateral tracking error 75% reduction, rearward amplification (RA) of lateral acceleration 18% reduction, and RA of yaw rate 37% reduction. This represents a substantial improvement in stability margins. The system was found to work well in conjunction with the braking-based stability control system of the towing vehicle with no negative interaction effects being observed. In all cases, the stability control system and the steering system improved the yaw stability of the combination.

This paper presents a comprehensive and flexible forward dynamic powertrain simulation tool, WARwick Powertrain Simulation Tool for ARchitectures 2 (WARPSTAR2), for modelling of conventional internal combustion engine, hybrid, and pure electric vehicles. WARPSTAR2 includes physical powertrain component models and their controllers, a hybrid supervisory controller, the driver, and the environment model. The physical powertrain component models are developed in Dymola, while the component controllers, the hybrid supervisory controllers, and the driver model are realized in MATLAB/Simulink. Thus the power of these two software tools is combined. A generalized fuzzy-logic-based supervisory controller is proposed for all hybrid electric vehicle (HEV) architectures so that all HEVs with different architectures share the same structure of supervisory controller. The generalized formation can be used for the supervisory controllers of different HEV architectures with varied parameter settings, thus facilitating the controller design process. The rule-based supervisory controller is also developed in WARPSTAR2. Simulation is carried out for different HEVs with these two supervisory controllers in the driving cycles. The results of engine and battery power usages with these two supervisory controllers are similar and the differences of predicted engine fuel consumptions between the two supervisory controllers are within 5 per cent.