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.

A model of driver steering control incorporating the driver's sensing of steering torque

Steering feel, or steering torque feedback, is widely regarded as an important aspect of the handling quality of a vehicle. Despite this, there is little theoretical understanding of its role. This paper describes an initial attempt to model the role of steering torque feedback arising from lateral tyre forces. The path-following control of a nonlinear vehicle model is implemented using a time-varying model predictive controller. A series of Kalman filters are used to represent the driver's ability to generate estimates of the system states from noisy sensory measurements, including the steering torque. It is found that under constant road friction conditions, the steering torque feedback reduces path-following errors provided the friction is sufficiently high to prevent frequent saturation of the tyres. When the driver model is extended to allow identification of, and adaptation to, a varying friction condition, it is found that the steering torque assists in the accurate identification of the friction condition. The simulation results give insight into the role of steering torque feedback arising from lateral tyre forces. The paper concludes with recommendations for further work. © 2011 Taylor & Francis.

Bias-free identification of a linear model-predictive steering controller from measured driver steering behavior

Recent developments in modeling driver steering control with preview are reviewed. While some validation with experimental data has been presented, the rigorous application of formal system identification methods has not yet been attempted. This paper describes a steering controller based on linear model-predictive control. An indirect identification method that minimizes steering angle prediction error is developed. Special attention is given to filtering the prediction error so as to avoid identification bias that arises from the closed-loop operation of the driver-vehicle system. The identification procedure is applied to data collected from 14 test drivers performing double lane change maneuvers in an instrumented vehicle. It is found that the identification procedure successfully finds parameter values for the model that give small prediction errors. The procedure is also able to distinguish between the different steering strategies adopted by the test drivers. © 2006 IEEE.

A path-following driver-vehicle model with neuromuscular dynamics, including measured and simulated responses to a step in steering angle overlay

An existing driver-vehicle model with neuromuscular dynamics is improved in the areas of cognitive delay, intrinsic muscle dynamics and alpha-gamma co-activation. The model is used to investigate the influence of steering torque feedback and neuromuscular dynamics on the vehicle response to lateral force disturbances. When steering torque feedback is present, it is found that the longitudinal position of the lateral disturbance has a significant influence on whether the drivers reflex response reinforces or attenuates the effect of the disturbance. The response to angle and torque overlay inputs to the steering system is also investigated. The presence of the steering torque feedback reduced the disturbing effect of torque overlay and angle overlay inputs. Reflex action reduced the disturbing effect of a torque overlay input, but increased the disturbing effect of an angle overlay input. Experiments on a driving simulator showed that measured handwheel angle response to an angle overlay input was consistent with the response predicted by the model with reflex action. However, there was significant intra-and inter-subject variability. The results highlight the significance of a drivers neuromuscular dynamics in determining the vehicle response to disturbances. © 2012 Copyright Taylor and Francis Group, LLC.

Linear quadratic game and non-cooperative predictive methods for potential application to modelling driver-AFS interactive steering control

This paper is concerned with the modelling of strategic interactions between the human driver and the vehicle active front steering (AFS) controller in a path-following task where the two controllers hold different target paths. The work is aimed at extending the use of mathematical models in representing driver steering behaviour in complicated driving situations. Two game theoretic approaches, namely linear quadratic game and non-cooperative model predictive control (non-cooperative MPC), are used for developing the driver-AFS interactive steering control model. For each approach, the open-loop Nash steering control solution is derived; the influences of the path-following weights, preview and control horizons, driver time delay and arm neuromuscular system (NMS) dynamics are investigated, and the CPU time consumed is recorded. It is found that the two approaches give identical time histories as well as control gains, while the non-cooperative MPC method uses much less CPU time. Specifically, it is observed that the introduction of weight on the integral of vehicle lateral displacement error helps to eliminate the steady-state path-following error; the increase in preview horizon and NMS natural frequency and the decline in time delay and NMS damping ratio improve the path-following accuracy. © 2013 Copyright Taylor and Francis Group, LLC.

Steering manufacturing firms towards service business model innovation

Increasingly, manufacturing firms are turning to services as a new way of creating and capturing value. Despite its potential benefits, many new product-service providers struggle to deploy service activities effectively, not least because they fail to refect the presence of service activities in their performance management systems. This article reports the results of an in-depth case study, which examines how manufacturers can steer the transition towards services. It shows that manufacturing firms need to emphasize two separate but related dimensions of the market performance of service activities: "service adoption," refecting the proportion of customers who purchase the manufacturer's services; and "service coverage," signaling the range of service elements or the comprehensiveness of the service contract that customers opt for. These two indicators, refecting service market performance, should be supplemented with a "complementarity index" designed to disclose whether the relationship between products and services is reinforcing or substitutive. When combined, these indicators allow manufacturing firms to deploy a service-based business model in an integrated and sustainable manner. © 2013 by The Regents of the University of California. All rights reserved.

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.