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International Journal of Automotive Technology > Volume 13(7); 2012 > Article
International Journal of Automotive Technology 2012;13(7): 1077-1088.
doi: https://doi.org/10.1007/s12239-012-0110-8
NONLINEAR TIRE-ROAD FRICTION CONTROL BASED ON TIRE MODEL PARAMETER IDENTIFICATION
F. YU, K. LI, J. CAO
Shanghai Jiao Tong University
ABSTRACT
A hierarchical control structure is a more suitable structural scheme for integrated chassis control. Generally, this type of structure has two main functions. The upper layer manages global control and force allocation, while the bottom layer allocates realized forces with 4 independent local tire controllers. The way to properly allocate these target forces poses a difficult task for the bottom layer. There are two key problems that require attention: obtaining the nonlinear time-varying coefficient of friction between the tire and different road surfaces and accurately tracking the desired forces from the upper layer. This paper mainly focuses on longitudinal tire-road friction allocation and control strategies that are based on the antilock braking system (ABS). Although it is difficult to precisely measure longitudinal tire-road friction forces for frequently changing road surface conditions, they can be estimated with a real-time measurement of brake force and angular acceleration at the wheels. The Magic Formula model is proposed as the reference model, and its key parameters are identified online using a constrained hybrid genetic algorithm to describe the evolution of tire-road friction with respect to the wheel slip. The desired wheel slip, with respect to the reference tire-road friction force from the top layer, is estimated with the inverse quadratic interpolation method. The tire-road friction controller of the extended anti-lock braking system (Ext-ABS) is designed through use of the nonlinear sliding mode control method. Simulation results indicate that acceptable modifications to changes in road surface conditions and adequate stability can be expected from the proposed control strategy.
Key Words: Parameter identification, Tire-road friction control, Constrained hybrid genetic algorithm, Nonlinear sliding mode control
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