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A benchmark study on the model-based estimation of the go-kart side-slip angle
Abstract and Figures
Nowadays, the active safety systems that control the dynamics of passenger cars usually rely on real-time monitoring of vehicle side-slip angle (VSA). The VSA can’t be measured directly on the production vehicles since it requires the employment of high-end and expensive instrumentation. To realiably overcome the VSA estimation problem, different model-based techniques can be adopted. The aim of this work is to compare the performance of different model-based state estimators, evaluating both the estimation accuracy and the computational cost, required by each algorithm. To this purpose Extended Kalman Filters, Unscented Kalman Filters and Particle Filters have been implemented for the vehicle system under analysis. The physical representation of the process is represented by a single-track vehicle model adopting a simplified Pacejka tyre model. The results numerical results are then compared to the experimental data acquired within a specifically designed testing campaign, able to explore the entire vehicle dynamic range. To this aim an electric go-kart has been employed as a vehicle, equipped with steering wheel encoder, wheels angular speed encoder and IMU, while an S-motion has been adopted for the measurement of the experimental VSA quantity.
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A correct reproduction of a motorcycle rider’s movements during driving is a crucial and the most influential aspect of the entire motorcycle–rider system. The rider performs significant variations in terms of body configuration on the vehicle in order to optimize the management of the motorcycle in all the possible dynamic conditions, comprising cornering and braking phases. The aim of the work is to focus on the development of a technique to estimate the body configurations of a high-performance driver in completely different situations, starting from the publicly available videos, collecting them by means of image acquisition methods, and employing machine learning and deep learning techniques. The technique allows us to determine the calculation of the center of gravity (CoG) of the driver’s body in the video acquired and therefore the CoG of the entire driver–vehicle system, correlating it to commonly available vehicle dynamics data, so that the force distribution can be properly determined. As an additional feature, a specific function correlating the relative displacement of the driver’s CoG towards the vehicle body and the vehicle roll angle has been determined starting from the data acquired and processed with the machine and the deep learning techniques.
The study of the human body and its movements is still a matter of great interest today. Most of these issues have as their fulcrum the study of the balance characteristics of the human body and the determination of its Centre of Mass. In sports, a lot of attention is paid to improving and analysing the athlete’s performance. Almost all the techniques for determining the Centre of Mass make use of special sensors, which allow determining the physical magnitudes related to the different movements made by athletes. In this paper, a markerless method for determining the Centre of Mass of a subject has been studied, comparing it with a direct widely validated equipment such as the Wii Balance Board, which allows determining the coordinates of the Centre of Pressure. The Motion Capture technique was applied with the OpenPose software, a Computer Vision method boosted with the use of Convolution Neural Networks. Ten quasi-static analyses have been carried out. The results have shown an error of the Centre of Mass position, compared to that obtained from the Wii Balance Board, which has been considered acceptable given the complexity of the analysis. Furthermore, this method, despite the traditional methods based on the use of balances, can be used also for prediction of the vertical position of the Centre of Mass.
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In this paper, the design of a vibration-based energy harvester mounted on the railway pantograph is discussed. The system aims to feed the diagnostic system of the pantograph-catenary interaction by recovering energy from the vibrations of the pantograph head itself. The design of the system is developed through a multiphysics finite element model, parametrized by means of experimental tests. The performance of the optimized solution, in terms of generated power, highlights the affability of the proposed solution.
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In this work, a reliable and effective method to predict the vehicle side-slip angle is given by means of an artificial neural network. It is well known that artificial neural networks are a very powerful modelling tool. They are largely used in many engineering fields to model complex and strongly non-linear systems. For this application, the network has to be as simple as possible in order to work in real-time within built-in applications such as active safety systems. The network has been trained with the data coming from a custom manoeuvre designed in order to keep the method simple and light from the computational point of view. Therefore, a 5-10-1 (input-hidden-output layer) network layout has been used. These aspects allow the network to give a proper estimation despite its simplicity. The proposed methodology has been tested by means of the CarSim® simulation package, which is considered one of the reference tools in the field of vehicle dynamics simulation. To prove the effectiveness of the method, tests have been carried out under different adherence conditions.
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The knowledge of the vehicle sideslip angle provides useful information about the state of the vehicle and it is often considered to increase the performance of the car as well as to develop safety systems, especially in the vehicle equipped with Torque Vectoring control systems. This paper describes two methods, based on the use of Kalman filters, to estimate the vehicle sideslip angle and the tire forces of a vehicle starting from the longitudinal and yaw velocity data. In particular, these data refer to on-track testing of a Range Rover Evoque performing ramp steer maneuvers at constant speed. The results of the sideslip estimation method are compared with the actual vehicle sideslip measured by a Datron sensor and are also used to estimate the tire lateral forces.
KeywordsSideslip angleKalman filterVehicleState estimationRandom walk method
