Search Results

The study of vehicle state estimation performance especially on the aspect of observer-based control for improving vehicle ride comfort and road handling is a challenging task for vehicle industry. Since vehicle roll behavior with various road excitations act an important part of driving safety, how to accurately obtain vehicle state under various driving scenes are of great concern. However, previous researches seldom consider coupling relation between vehicle vertical and lateral response with steering input under various road excitation. To address this issue, comprehension analyses on vehicle roll state estimation with coupled input are present in this paper. A full-car nonlinear Takagi-Sugeno (T-S) fuzzy model is first created to describe vehicle lateral and vertical coupling dynamics.

This paper presents a road classifier based on the system response with consideration of the tire enveloping. The aim is to provide an easily applicable yet accurate road classification approach for automotive engineers. For this purpose, tire enveloping effect is firstly modeled based on the flexible roller contact (FRC) theory, then transfer functions between road input and commonly used suspension responses i.e. the sprung mass acceleration, unsprung mass acceleration, and rattle space, are calculated for a quarter vehicle model. The influence of parameter variations, vehicle velocity, and measurement noise on transfer functions are comprehensively analyzed to derive the most suitable system response thereafter. In addition, this paper proposes a vehicle speed correction mechanism to further improve the classification accuracy under complex driving conditions.

The accuracy of road input identifiaction for autonomous vehicles (AVs) system, especially in state-based AVs control for improving road handling and ride comfort, is a challenging task for the intelligent transport system. Due to the high fatality rate caused by inaccurate state-based control algorithm, how to precisely and effectively acquire road rough information and chose the reasonable road-based control algorithm become a hot topic in both academia and industry. Uncertainty is unavoidable for AVs system, e.g., varying center of gravity (C.G.) of sprung mass, controllable suspension damping force or variable spring stiffness. To tackle the above mentioned, this paper develops a novel observer approach, which combines unscented Kalman filter (UKF) and Minimum Model Error (MME) theory, to optimize the estimation accuracy of the road rough for AVs system. A full-car nonlinear model and road profile model are first established.

The study of vehicle coupling state estimation accuracy especially in observer-based vehicle chassis control for improving road handling and ride comfort is a challenging task for vehicle industry under various driving conditions. Due to a large amount of life safety arising from vehicle roll behavior, how to precisely acquire vehicle roll state and rapidly provide for the vehicle control system are of great concern. Simultaneously, uncertainty is unavoidable for various aspects of a vehicle system, e.g., varying sprung mass, moment of inertia and position of the center of gravity. To deal with the above issues, a novel dual observer approach, which combines adaptive Unscented Kalman Filter (AUKF) and Takagi-Sugeno (T-S), is proposed in this paper. A full-car nonlinear model is first established to describe vehicle lateral and vertical coupling roll behavior under various road excitation.

The accuracy of chassis control for intelligent electric vehicles (IEVs), especially in road-based IEVs control for improving road holding and ride comfort, is a challenging task for the intelligent transport system. Due to the high fatality rate caused by inaccurate road-based control algorithms, how to precisely and effectively choose a reasonable road-based control algorithm become a hot topic in both academia and industry. To address and improve the performance of road holding and ride comfort of IEVs by using a semi-active suspension system, an adaptive sliding mode control (ASMC) algorithm-based road information is proposed to realize the overall performance of the intelligent vehicle chassis system in the paper. Firstly, the models of road excitation and equivalent hybrid control of a quarter semi-active suspension system are established.