Title: A stochastic model predictive control approach for modelling human driver steering control

Authors: Ting Qu; Hong Chen; Yan Ji; Hongyan Guo; Yueting Xu

Addresses: State Key Laboratory of Automotive Simulation and Control, Jilin University (Campus NanLing), Renmin Str. 5988, Changchun 130025, China ' State Key Laboratory of Automotive Simulation and Control, Jilin University (Campus NanLing), Renmin Str. 5988, Changchun 130025, China; Department of Control Science and Engineering, Jilin University (Campus NanLing), Renmin Str. 5988, Changchun 130025, China ' Department of Control Science and Engineering, Jilin University (Campus NanLing), Renmin Str. 5988, Changchun 130025, China ' Department of Control Science and Engineering, Jilin University (Campus NanLing), Renmin Str. 5988, Changchun 130025, China ' Department of Control Science and Engineering, Jilin University (Campus NanLing), Renmin Str. 5988, Changchun 130025, China

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.

Keywords: 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.

DOI: 10.1504/IJVD.2016.075770

International Journal of Vehicle Design, 2016 Vol.70 No.3, pp.249 - 277

Available online: 04 Apr 2016 *

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