Design of an Integrated Vehicle Chassis Control System with Driver Behavior Identification

Hindawi Publishing Corporation Mathematical Problems in Engineering Volume 2015, Article ID 954514, 12 pages http://dx.doi.org/10.1155/2015/954514 Research Article Design of an Integrated Vehicle Chassis Control System with Driver Behavior Identification Bing Zhu,1,2 Yizhou Chen,1 Jian Zhao,1 and Yunfu Su1 1 State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China Key Laboratory of Bionic Engineering of Ministry of Education, Jilin University, Changchun 130022, China 2 Correspondence should be addressed to Jian Zhao; Received 27 April 2015; Accepted 18 August 2015 Academic Editor: Yannis Dimakopoulos Copyright © 2015 Bing Zhu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. An integrated vehicle chassis control strategy with driver behavior identification is introduced in this paper. In order to identify the different types of driver behavior characteristics, a driver behavior signals acquisition system was established using the dSPACE real-time simulation platform, and the driver inputs of 30 test drivers were collected under the double lane change test condition. Then, driver behavior characteristics were analyzed and identified based on the preview optimal curvature model through genetic algorithm and neural network method. Using it as a base, an integrated chassis control strategy with active front steering (AFS) and direct yaw moment control (DYC) considering driver characteristics was established by model predictive control (MPC) method. Finally, simulations were carried out to verify the control strategy by CarSim and MATLAB/Simulink. The results show that the proposed method enables the control system to adjust its parameters according to the driver behavior identification results and the vehicle handling and stability performance are significantly improved. 1. Introduction In recent years, improving the active safety performance of vehicles is what researchers have been working on. With the development of electronic control technology, many active safety systems such as antilock braking system (ABS), traction control system (TCS), electronic stability control (ESC), active front steering (AFS), and four-wheel steering (4WS) have been widely equipped on vehicles to ensure safer and more stable driving experience. However, the potential conflicts among the active control systems increase when they are combined without coordination [1–3]. Thus, there have been plenty of attempts to integrate the chassis control subsystems, for instance, the Integrated Chassis Control (ICC), Unified Chassis Control (UCC), and Vehicle Dynamics Management (VDM) [4–6]. With the implementation of integrated control systems, the interference and coupling among dynamic subsystems are effectively eliminated and the stability performance of vehicle is significantly improved. Nevertheless, integrated control systems nowadays 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 the active control system have strong coupling on each other while controlling the vehicle. According to a survey sponsored by National Highway Traffic Safety Administration (NHTSA) of USA, driver’s mistake accounts for 90% or more of all the crashes recorded [7]. Therefore, human driver’s characteristics should be involved in the process where the integrated control system is developed. As the primary control element within the traditional driver-vehicle system, the role of human driver has been studied by a plenty of scientists and researchers. The driver behavior includes driver’s sensing, judging, reasoning, deciding, and finally operating the vehicle to turn, accelerate, and brake with strong randomicity, adaptivity, discreteness, and variability. Currently, research is mainly carried out in the aspects of driver behavior modeling, the identification of fatigue, intelligent traffic control, and advanced driver assistance systems (ADAS) [8]. Koh et al. present a tire slip-angle based speed control race driver model through analyzing the vehicle-driver 2 interaction at limit handling [9]. Miyajima et al. modeled driver’s behavior such as turning the steering wheel or hitting the pedal with Gaussian mixture model (GMM) to identify different kinds of drivers. Compared with the method using raw pedal operation signals, spectral analysis method proposed shows a far better accuracy [10]. Bolia et al. proposed a two-level preview driver steering control model. The outer loop focuses on path following while the inner one tries to capture driver’s physical behavior [11]. Lin et al. used several sophisticated artificial neural network architectures to develop driver models in a Driver-Vehicle-Environment (DVE) system [12]. Sathyanarayana et al. proposed a “context and driver aware” (CDA) active vehicle system combining GMM, universal background model, and likelihood maximization based on information fusion to identify driver status and predict driver’s distracted behavior [13]. Profound and enlightening works above endeavored to explain what kind of a role driver is playing in a driver-vehicle closedloop system. With further research, early efforts have been made to design the active control system on the basis of driver’s characteristics. Macadam studied the physiological limits and physical characteristics of drivers in detail and then established the lateral and longitudinal manipulating model of drivers [14]. Hoult and Cole built a neuromuscular linear model which considers the coactivation of neuromuscular system, muscle, body, and vehicle [15]. Chai et al. described a method to adjust the parameters of steer-by-wire (SBW) system according to the driver’s steer characteristics, which could be estimated from experimental data based on the general driver’s model [16]. Raksincharoensak et al., Japanese researchers, designed the direct yaw moment control system based on the identification of driver’s intention and the performance of the vehicle is significantly improved [17]. In the literature [18], a driver behavior signal capturing system was introduced, based on which the riding comfort and safety of the vehicle are enhanced. Fu et al. integrated a driver model with a run-off-road recovery controller considering driver’s target planning, pursuit behavior, compensate behavior, and physical limitations [19]. Keen and Cole proposed a steering controller based on linear model predictive control. A formal system identification procedure is applied to avoid bias from the closed-loop operation of the driver-vehicle system [20]. Although the driver behavior identification methods have made remarkable progress, in practical applications, the driver behavior observation equipment mentioned above is (...truncated)