Spectral analysis of suspension system of a commercial city bus

There is an ever increasing demand for intelligent and efficient urban vehicle systems that fulfill several requirements, e.g., low cost maintainability and high passenger comfort. Concerning these goals reliable methods are needed to model and to evaluate the imposed performances. In this paper a spectral analysis of the suspension system of a commercial city bus is presented. Based on experimental data taken on a city bus, the vibrations emerging on the wheels and the body are analyzed in the frequency domain. The goal of the analysis is to characterize the main eigenfrequencies of the suspension system and its damping in amplitude and also to evaluate both the road and the suspension system in terms of passenger comfort according to ISO standards. © 2016 IEEE

Identification of the nonlinear steering dynamics of an autonomous vehicle

Automated driving applications require accurate vehicle specific models to precisely predict and control the motion dynamics. However, modern vehicles have a wide array of digital and mechatronic components that are difficult to model, manufactures do not disclose all details required for modelling and even existing models of subcomponents require coefficient estimation to match the specific characteristics of each vehicle and their change over time. Hence, it is attractive to use data-driven modelling to capture the relevant vehicle dynamics and synthesise model-based control solutions. In this paper, we address identification of the steering system of an autonomous car based on measured data. We show that the underlying dynamics are highly nonlinear and challenging to be captured, necessitating the use of data-driven methods that fuse the approximation capabilities of learning and the efficiency of dynamic system identification. We demonstrate that such a neural network based subspace-encoder method can successfully capture the underlying dynamics while other methods fall short to provide reliable results