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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.

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 accuracy and range of chassis control for a four in-wheel motor (IWM)-driven electric vehicles (EVs), especially in observer-based EVs control for improving road handling and ride comfort, is a challenging task for the IWM-driven vehicle system. Due to the high fatality rate caused by inaccurate state-based control algorithm, how to precisely acquire movement state and chose the reasonable observer-based control algorithm for IWM-driven EVs become a hot topic in both academia and industry. Simultaneously, uncertainty is always existing, e.g., varying road excitation, variable system parameters or nonlinear structure. Meanwhile, the coupling effects between the non-ideal IWM actuator and vehicle are ignored under the assumption of an ideal actuator.

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.