A stochastic model predictive control approach for modelling human driver steering control. (2016)

• Record Type: Journal Article
• Authors: Qu, Ting; Chen, Hong; Ji, Yan; Guo, Hongyan; Xu, Yueting
• Is Part Of: International journal of vehicle design. Volume 70:Number 3(2016)
• Page Start: 249
• Page End: 277
• Publication Date: 2016
• Subjects: driver steering control -- driver modelling -- SMPC -- stochastic MPC -- model predictive control -- internal vehicle dynamics -- uncertainties -- road friction -- random variation -- experimental validation -- stochastic modelling -- simulation -- active safety -- vehicle safety -- dynamic modelling -- driver perceptions -- road conditions -- physiological constraints; Motor vehicles -- Design and construction -- Periodicals; 629.231
• Languages: English
• ISSNs: 0143-3369

Abstract

The simulation-based design and development of various vehicle active safety systems necessitate an enhanced understanding of the driver-vehicle-road system, and particular attention is paid to the improved modelling of the driver driving control characteristics. In this paper, a novel driver steering control model based on stochastic model predictive control (SMPC) is proposed to effectively incorporate the random variation characteristics of road friction. A multi-point driver preview approach is employed to represent the driver's perception of the desired path. An internal vehicle dynamic model with parameter uncertainty in the friction coefficient is formulated to represent the knowledge and adaptation of the driver to the variations in road conditions. A transport delay is applied to reflect the driver's physiological constraints. The SMPC method is then used to minimise a weighted cost function. Simulation and experimental validations are presented to demonstrate the effectiveness and practicability of the proposed modelling framework.