Identification of the steering control behaviour of five test subjects following a randomly curving path in a driving simulator

The paper is concerned with the identification of theoretical preview steering controllers using data obtained from five test subjects in a fixed-base driving simulator. An understanding of human steering control behaviour is relevant to the design of autonomous and semi-autonomous vehicle controls. The driving task involved steering a linear vehicle along a randomly curving path. The theoretical steering controllers identified from the data were based on optimal linear preview control. A direct-identification method was used, and the steering controllers were identified so that the predicted steering angle matched as closely as possible the measured steering angle of the test subjects. It was found that identification of the driver's time delay and noise is necessary to avoid bias in identification of the controller parameters. Most subjects' steering behaviour was predicted well by a theoretical controller based on the lateral/yaw dynamics of the vehicle. There was some evidence that an inexperienced driver's steering action was better represented by a controller based on a simpler model of the vehicle dynamics, perhaps reflecting incomplete learning by the driver. Copyright © 2014 Inderscience Enterprises Ltd

Active steering of a tractor-semi-trailer

This paper develops a path-following steering control strategy for an articulated heavy goods vehicle. The controller steers the axles of the semi-trailer so that its rear end follows the path of the fifth wheel coupling: for all paths and all speeds. This substantially improves low-speed manoeuvrability, off-tracking, and tyre scrubbing (wear). It also increases high-speed stability, reduces 'rearward amplification', and reduces the propensity to roll over in high-speed transient manoeuvres. The design of a novel experimental heavy goods vehicle with three independent hydraulically actuated steering axles is presented. The path-following controller is tested on the experimental vehicle, at low and high speeds. The field test results are compared with vehicle simulations and found to agree well. The benefits of this steering control approach are quantified. In a low-speed 'roundabout' manoeuvre, low-speed off-tracking was reduced by 73 per cent, from 4.25 m for a conventional vehicle to 1.15 m for the experimental vehicle; swept-path width was reduced by 2 m (28 per cent); peak scrubbing tyre forces were reduced by 83 per cent; and entry tail-swing was eliminated. In an 80 km/h lane-change manoeuvre, peak path error for the experimental vehicle was 33 per cent less than for the conventional vehicle, and rearward amplification of the trailer was 35 per cent less. Increasing the bandwidth of the steering actuators improved the high-speed dynamic performance of the vehicle, but at the expense of increased oil flow

Parameter measurement for heavy-vehicle fuel consumption modelling

A mathematical model is developed to predict the energy consumption of a heavy vehicle. It includes the important factors of heavy-vehicle energy consumption, namely engine and drivetrain performances, losses due to accessories, aerodynamic drag, rolling resistance, road gradients, and driver behaviour. Novel low-cost testing methods were developed to determine engine and drivetrain characteristics. A simple drive cycle was used to validate the model. The model is able to predict the fuel use for a 371 tractor-semitrailer vehicle over a 4 km drive cycle within 1 per cent. This paper demonstrates that accurate and reliable vehicle benchmarking and model parameter measurement can be achieved without expensive equipment overheads, e.g. engine and chassis dynamometers