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Transient force and surface pressure data has been measured on a range of simple geometric shapes in order to gain an understanding of the complex time dependent and separated flow around a vehicle when subjected to a crosswind. The experiments were carried out using the Cranfield University model crosswind facility. It is found that the leeward face is the dominant area of transient activity. Maximum and minimum peak yawing moments at gust entry and exit are compared

In the current literature, the research studies on the trajectory tracking control and stability control strategy for autonomous vehicles in limited condition mostly focus on the yaw plane control, but few of the studies have considered the combined control performance of trajectory tracking, yaw and roll stability, and the roll stability is critical under the extreme cornering condition for autonomous vehicles. Aiming at the above shortages, this study designs the model predictive control (MPC) strategy for the autonomous vehicles under the limited handling condition, which integrates the front and rear wheel active steering control, four-wheel independent drive and braking control and active suspension control to comprehensively improve the trajectory tracking accuracy, yaw plane stability and roll plane stability of the vehicle under the extreme condition.

The effect of aerodynamic lift on both straight line stability and lane change manoeuvrability of several small and medium sized European passenger cars has been determined from subjective track tests. The straight line and manoeuvring performance degrades with increasing lift and decreasing pitching moment. Increasing speed exacerbates the problem.

The motion of a vehicle along a desired path is possible due to steering action of the driver. Hence, vehicle dynamics and control simulations should take into consideration the action of the driver. This work presents a preview based vehicle steering controller using Neural Networks which can be used in the vehicle lateral dynamics simulations. The training data for the Neural Network is being obtained using a steering controller from the existing literature and its gains are determined using Optimization. Three different architectures are being designed and conclusions are presented. These Neural Network models are validated by testing against real track data.

The aim of this paper is to develop a new concept of unconventional airship based on morphing a lenticular shape while preserving the volumetric dimension. Lenticular shape is known to have relatively poor aerodynamic characteristics. It is also well known to have poor static and dynamic stability after the certain critical speed. The new shape presented in this paper is obtained by extending one and reducing the other direction of the original lenticular shape. The volume is kept constant through the morphing process. To improve the airship performance, four steps of morphing, starting from the lenticular shape, were obtained and compared in terms of aerodynamic characteristics, including drag, lift and pitching moment, and stability characteristics for two different operational scenarios. The comparison of the stability was carried out based on necessary deflection angle of the part of tail surface.

The vehicle dynamics control systems are traditionally based upon utilizing wheel brakes as actuators. However, there has been recently strong interest in the automotive industry for introduction of other vehicle dynamics actuators, in order to improve the overall vehicle stability, responsiveness, and agility features. This paper considers various actuators such as active rear and central differentials and active front and rear steering, and proposes design of related yaw rate control systems. Different control subsystems such as reference model, feedback and feedforward control, allocation algorithm, and time-varying controller limit are discussed. The designed control systems are verified and compared by computer simulation for double lane change and slalom maneuvers.

The interactions between automatic controls, physics, and driver is an important step towards highly automated driving. This study investigates the dynamical interactions between human-selected driving modes, vehicle controller and physical plant parameters, to determine how to optimally adapt powertrain control to different human-like driving requirements. A cyber-physical system (CPS) based framework is proposed for co-design optimization of the physical plant parameters and controller variables for an electric powertrain, in view of vehicle’s dynamic performance, ride comfort, and energy efficiency under different driving modes. System structure, performance requirements and constraints, optimization goals and methodology are investigated. Intelligent powertrain control algorithms are synthesized for three driving modes, namely sport, eco, and normal modes, with appropriate protocol selections. The performance exploration methodology is presented.