Search Results (5)

This paper presents a design method of a suspension controller with an adaptive feedforward algorithm for ride comfort enhancement and motion sickness mitigation. Recently, it was shown that motion sickness is caused by combined heave and pitch motions of a sprung mass within the range of 0.8 and 8 Hz. For this reason, it is necessary to design a suspension controller for the purpose of reducing the heave and pitch vibration of a sprung mass within this range. To represent the heave acceleration and the pitch rate of a sprung mass, a 4-DOF half-car model is adopted as a vehicle model. For easy implementation in a real vehicle, a static output feedback control is adopted instead of a full-state one. To reduce the heave acceleration of a sprung mass for ride comfort enhancement, a linear quadratic SOF controller is designed. To reduce the pitch rate of a sprung mass for motion sickness mitigation, a filtered-X LMS algorithm is applied. To validate the method, simulation on vehicle simulation software is conducted. From the simulation results, it is shown that the proposed method is effective for ride comfort enhancement and motion sickness mitigation. Full article

This paper presents a method to design active suspension controllers for a 7-Degree-of-Freedom (DOF) full-car (FC) model from controllers designed with a 2-DOF quarter-car (QC) one. A linear quadratic regulator (LQR) with 7-DOF FC model has been widely used for active suspension control. However, it is too hard to implement the LQR in real vehicles because it requires so many state variables to be precisely measured and has so many elements to be implemented in the gain matrix of the LQR. To cope with the problem, a 2-DOF QC model describing vertical motions of sprung and unsprung masses is adopted for controller design. LQR designed with the QC model has a simpler structure and much smaller number of gain elements than that designed with the FC one. In this paper, several controllers for the FC model are derived from LQR designed with the QC model. These controllers can give equivalent or better performance than that designed with the FC model in terms of ride comfort. In order to use available sensor signals instead of using full-state feedback for active suspension control, LQ static output feedback (SOF) and linear quadratic Gaussian (LQG) controllers are designed with the QC model. From these controllers, observer-based controllers for the FC model are also derived. To verify the performance of the controllers for the FC model derived from LQR and LQ SOF ones designed with the QC model, frequency domain analysis is undertaken. From the analysis, it is confirmed that the controllers for the FC model derived from LQ and LQ SOF ones designed with the QC model can give equivalent performance to those designed with the FC one in terms of ride comfort. Full article

In a rail vehicle, fatigue fracture causes a significant number of failures in the coil spring of the suspension system. In this work, the origin of these failures is examined by studying the rail wheel–track interaction, the modal analysis of the coil springs and the stresses induced during operation. The spring is tested experimentally, and a mathematical model is developed to show its force vs. displacement characteristics. A vertical 10-degree-of-freedom (DOF) mathematical model of a full-scale railway vehicle is developed, showing the motions of the car body, bogies and wheelsets, which are then combined with a track. The springs show internal resonances at nearly 50–60 Hz, where significant stresses are induced in them. From the stress result, the weakest position in the innerspring is identified and a few guidelines are proposed for the reduction of vibration and stress in rail vehicles. Full article

Assessing passenger cars’ dynamic performance is a critical aspect for car industries, due to its impact on the overall vehicle safety evaluation and the subjective nature of the involved handling and comfort metrics. Accordingly, ISO standards, such as ISO 4138 and ISO 3888, define several specific driving tests to assess vehicle dynamics performance objectively. Consequently, proper evaluation of the dynamic behaviour requires measuring several physical quantities, including accelerations, speed, and linear and angular displacements obtained after instrumenting a vehicle with multiple sensors. This experimental activity is highly demanding in terms of hardware costs, and it is also significantly time-consuming. Several approaches can be considered for reducing vehicle development time. In particular, simulation software can be exploited to predict the approximate behaviour of a vehicle using virtual scenarios. Moreover, motion platforms and detail-scalable numerical vehicle models are widely implemented for the purpose. This paper focuses on a customized simulation environment developed in C++, which exploits the advantages of object-oriented programming. The presented framework strives to perform concurrent simulations of vehicles with different characteristics such as mass, tyres, engine, suspension, and transmission systems. Within the proposed simulation framework, we adopted a hierarchical and modular representation. Vehicles are modelled by a 14 degree-of-freedom (DOF) full-vehicle model, capable of capturing the dynamics and complemented by a set of scalable-detail models for the remaining sub-systems such as tyre, engine, and steering system. Furthermore, this paper proposes the usage of autonomous virtual drivers for a more objective evaluation of vehicle dynamic performances. Moreover, to further evaluate our simulator architecture’s efficiency and assess the achieved level of concurrency, we designed a benchmark able to analyse the scaling of the performances with respect to the number of different vehicles during the same simulation. Finally, the paper reports the proposed simulation environment’s scalability resulting from a set of different and varying driving scenarios. Full article

Considering the importance of increasing driving safety, the study of safety is a popular and critical topic of research in the vehicle industry. Vehicle roll behavior with sudden steering input is a main source of untripped rollover. However, previous research has seldom considered road excitation and its coupled effect on vehicle lateral response when focusing on lateral and vertical dynamics. To address this issue, a novel method was used to evaluate effects of varying road level and steering wheel input on vehicle roll behavior. Then, a 9 degree of freedom (9-DOF) full-car roll nonlinear model including vertical and lateral dynamics was developed to study vehicle roll dynamics with or without of road excitation. Based on a 6-DOF half-car roll model and 9-DOF full-car nonlinear model, relationship between three-dimensional (3-D) road excitation and various steering wheel inputs on vehicle roll performance was studied. Finally, an E-Class (SUV) level car model in CARSIM^® was used, as a benchmark, with and without road input conditions. Both half-car and full-car models were analyzed under steering wheel inputs of 5°, 10° and 15°. Simulation results showed that the half-car model considering road input was found to have a maximum accuracy of 65%. Whereas, the full-car model had a minimum accuracy of 85%, which was significantly higher compared to the half-car model under the same scenario. Full article