Research On The Integrated Vehicle Chassis Control With Driver Behavior Identification

The integrated vehicle chassis control system, which aims at coordinating multiple standalone subsystems to optimize vehicle performance, has become a focus in current research. The integrated vehicle chassis control systems nowadays, however, are generally designed in the uniform mode, lacking the consideration of the influence that drivers exert on control systems. As a matter of fact, the driver and active control system have strong coupling on each other while controlling the vehicle, the human driver’s characteristics consequently should be involved in the process where the integrated system is development. Supported by the National Natural Science Foundation of China(51105169, 51475206), this paper designs an integrated vehicle chassis control system with driver behavior identification. Firstly, the driver behavior signals acquisition system is designed and established, based on which, the input data of different kinds of drivers along with vehicle signals are collected under typical working condition. Then, the driver behavior characteristics are analyzed through bionic intelligent algorithm. Based on which, the integrated vehicle chassis control strategy of active front steering(AFS) and electronic stability control(ESC) based on active braking using improved inverse nyquist array method considering driver behavior is established. Finally, simulations are carried out to verify and analyze the control strategy by MATLAB/Simulink and Car Sim co-simulation.This paper carried out the following tasks,(1) The acquisition of different kinds of drivers’ operation signalsIn order to analyze, identify and quantify driver’s behavior, the driver behavior signals acquisition system based on d SPACE DS1006 real-time simulation platform is designed and established. Driver behavior signals such as steering wheel angle, opening of acceleration pedal and braking pedal and vehicle motion signals are collected using the driver behavior signals acquisition system under typical working condition.(2) The identification of driver behavior based on bionic intelligent algorithmOptimal preview curvature model is introduced to describe driver’s behavior. The preview time, neural delay time and muscle inertial delay time of drivers are used to represent the difference among drivers’ behavior. Based on which, improved Particle Swarm Optimization(PSO) and Back-Propagation(BP) neural network are used to design the identification strategy of driver behavior on-line and off-line, respectively. The identification results are analyzed and verified.(3) Design of the integrated vehicle chassis control strategy based on identification ofdriver behaviorThe architecture of integrated control system for AFS and ESC integration with driver behavior identification is proposed. The reference model of integrated chassis control system includes the 2-Degree-of-Freedom(DOF) vehicle model and driver behavior identification module. The analysis and identification results of driver along with the vehicle state could be used to predict driver’s steering angle at the next sample time as the input of the 2-DOF vehicle model. The interference and coupling of AFS and ESC is analyzed and improved inverse nyquist array(INA) method considering the variation of vehicle velocity and nonlinear effects of cornering stiffness is developed to design the integrated control system and decouple the system effectively.(4) The verification of the control strategyThe co-simulation platform of MATLAB/Simulink and Car Sim is established, based on which, double lane change and slalom tests are chosen to verify the control strategy under different vehicle velocity and tire-road adhesion coefficient. The simulation results show that the integrated chassis control system with driver behavior identification could effectively improve vehicle’s trajectory-tracking performance, enhance handling and stability and meanwhile, relieve driver’s workload.