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A levitation system based on sidewall electrodynamic suspension (EDS) is considered for a capsule vehicle, which is a next-generation high-speed transportation system currently being studied. This levitation system does not require controlling of the gap between the guideway and the vehicle on which the superconducting electromagnet is mounted. However, when the vehicle is operated in a levitated state, the ride comfort is worse than that of the levitation system based on electromagnetic suspension (EMS), making it necessary to develop methods that can ensure good riding comfort. In addition, because the EDS system is complex and nonlinear with a combination of electromagnetics and mechanical dynamics, it is complicated to analyze the dynamic characteristics of the capsule vehicle, and the corresponding numerical analysis is time-consuming. Therefore, to easily understand the running dynamics of a capsule vehicle in the sidewall EMS system, the magnetic suspension characteristics corresponding to the primary suspension are simply modeled by considering the levitation stiffness in the vertical direction and the guidance stiffness in the lateral direction, similar to that in the case of the mechanical suspension. In this study, mathematical models of the levitation and guidance stiffnesses with respect to the speed and position of a vehicle body running at high speeds in a levitated state in the sidewall EDS system were derived for three design proposals of the levitation coil. The dynamic behavior of the vehicle based on the three design proposals was investigated by simulating a capsule vehicle model with 15 degrees of freedom. [ABSTRACT FROM AUTHOR]

Novel drivetrain concepts such as electric direct drives can improve vehicle dynamic control due to faster, more accurate, and more flexible generation of wheel individual propulsion and braking torques. Exact and robust estimation of vehicle state of motion in the presence of unknown disturbances, such as changes in road conditions, is crucial for realization of such control systems. This article shows the design, tuning, implementation, and test of a state estimator with individual tire model adaption for direct drive electric vehicles. The vehicle dynamics are modeled using a double-track model with an adaptive tire model. State-of-the-art sensors, an inertial measurement unit, steering angle, wheel speed, and motor current sensors are used as measurements. Due to the nonlinearity of the vehicle model, an Unscented Kalman Filter (UKF) is used for simultaneous state and parameter estimation. To simplify the difficult task of UKF tuning, an optimization-based method using real-vehicle data is utilized. The UKF is implemented on an electronic control unit and tested with real-vehicle data in a hardware-in-the-loop simulation. High precision even in severe driving maneuvers under various road conditions is achieved. Nonlinear state and parameter estimation for all wheel drive electric vehicles using UKF and optimization-based tuning is shown to provide high precision with minimal manual tuning effort. [ABSTRACT FROM AUTHOR]

This paper presents a novel modeling method for the control design of autonomous vehicle systems. The goal of the method is to provide a control-oriented model in a predefined Linear Parameter Varying (LPV) structure. The scheduling variables of the LPV model through machine-learning-based methods using a big dataset are selected. Moreover, the LPV model parameters through an optimization algorithm are computed, with which accurate fitting on the dataset is achieved. The proposed method is illustrated on the nonlinear modeling of the lateral vehicle dynamics. The resulting LPV-based vehicle model is used for the control design of path following functionality of autonomous vehicles. The effectiveness of the modeling and control design methods through comprehensive simulation examples based on a high-fidelity simulation software are illustrated. [ABSTRACT FROM AUTHOR]