Gabor Rodonyi

14030500800

Publications - 8

Data-driven linear parameter-varying modelling of the steering dynamics of an autonomous car

Gabor Rodonyi R. Tóth Peter Koros Dániel Pup Ádám Kisari Zsombor Vigh J. Bokor

Publication Name: IFAC Papersonline

Publication Date: 2021-07-01

Volume: 54

Issue: 8

Page Range: 20-26

Description:

Developing automatic driving solutions and driver support systems requires accurate vehicle specific models to describe and predict the associated motion dynamics of the vehicle. Despite of the mature understanding of ideal vehicle dynamics, which are inherently nonlinear, modern cars are equipped with a wide array of digital and mechatronic components that are difficult to model. Furthermore, due to manufacturing, each car has its personal motion characteristics which change over time. Hence, it is important to develop data-driven modelling methods that are capable to capture from data all relevant aspects of vehicle dynamics in a model that is directly utilisable for control. In this paper, we show how Linear Parameter-Varying (LPV) modelling and system identification can be applied to reliably capture personalised model of the steering system of an autonomous car based on measured data. Compared to other nonlinear identification techniques, the obtained LPV model is directly utilisable for powerful controller synthesis methods of the LPV framework.

Open Access: Yes

DOI: 10.1016/j.ifacol.2021.08.575

Identification of the nonlinear steering dynamics of an autonomous vehicle

Gabor Rodonyi R. Tóth M. Schoukens Peter Koros Dániel Pup Ádám Kisari G. I. Beintema Zsombor Vigh Alexandros Soumelidis J. Bokor

Publication Name: IFAC Papersonline

Publication Date: 2021-07-01

Volume: 54

Issue: 7

Page Range: 708-713

Description:

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.

Open Access: Yes

DOI: 10.1016/j.ifacol.2021.08.444

Characterization of Model Uncertainty Features Relevant to Model Predictive Control of Lateral Vehicle Dynamics

Gabor Rodonyi Dániel Pup Ádám Kisari Zsombor Vigh Alexandros Soumelidis J. Bokor

Publication Name: 2020 23rd IEEE International Symposium on Measurement and Control in Robotics Ismcr 2020

Publication Date: 2020-10-15

Volume: Unknown

Issue: Unknown

Page Range: Unknown

Description:

The information about a system's dynamics represented by measurement data sets are often confined to regions of restricted operations where the system is not sufficiently excited for model identification purposes. Experiments performed in closed-loop with safety constraints allow only for reduced order modeling. In the paper, a set of low order models are identified from real experimental data of the lateral dynamics of an electric passenger car. Low order models are advantageous for on-line computation in model-based control, though uncertainty due to neglected dynamics may deteriorate control performance and constraint satisfaction. The effect of uncertainty is analyzed by controller cross-validation where a controller designed based on one model is evaluated on other models playing the role of the true system. This method allows us to qualify not only model-controller pairs, but to determine the properties of input data and model uncertainty, which lead to more useful data sets, more robust and better performing controllers than the others.

Open Access: Yes

DOI: 10.1109/ISMCR51255.2020.9263745

Experimental verification of a control system for autonomous navigation

Gabor Rodonyi Peter Koros Dániel Pup Ádám Kisari Benedek Szucs Alexandros Soumelidis J. Bokor

Publication Name: IFAC Papersonline

Publication Date: 2020-01-01

Volume: 53

Issue: 2

Page Range: 14273-14278

Description:

A flexible architecture is developed with the purpose of supporting education and research on the field of autonomous vehicles. A pure electric vehicle is equipped with on-board computational units, sensors and actuator interfaces. This paper presents the current status of the control system and its validation by means of navigation experiments. With the cascade control architecture, problems of actuator dead-zone, sensor offset errors, path tracking and redesign for obstacle avoidance are addressed.

Open Access: Yes

DOI: 10.1016/j.ifacol.2020.12.1171

Structure Selection and LPV Model Identification of a Car Steering Dynamics⁎

Gabor Rodonyi Zoltán Szabó Peter Koros Dániel Pup Alexandros Soumelidis J. Bokor

Publication Name: Unknown

Publication Date: 2018-01-01

Volume: 51

Issue: 15

Page Range: 1086-1091

Description:

A Linear Parameter-Varying (LPV), discrete-time black box model of an electric power assisted steering system of a passenger car is identified from open-loop step response measurement data. The goal is to provide a nominal model for control design and analysis that is able to describe the principal characteristics of the system in the whole region of steering angle and speed range of 3 to 30 km/h. Examining a set of experimental data by using classical linear time-invariant black box modeling and validation techniques, the structure of the LPV model is determined. The parameters of the model are identified based on minimizing a quadratic error criterion by nonlinear optimization algorithms.

Open Access: Yes

DOI: 10.1016/j.ifacol.2018.09.049

Vibration analysis of a suspension system subject to high level of measurement noise

Gabor Rodonyi György Istenes Ferenc Szauter

Publication Name: 2017 4th International Conference on Control Decision and Information Technologies Codit 2017

Publication Date: 2017-11-08

Volume: 2017-January

Issue: Unknown

Page Range: 881-886

Description:

Using only vertical acceleration measurements for the sprung and unsprung masses of a suspension system of a commercial city bus, the goal of the paper is to develop an analysis method to find the vibration modes of the mechanical system from data measured during real life operation. The identified vibration modes can be used to (in)validate first principle physical models of the system, while the identified ARMA models can be used to develop uncertainty models. The challenge in the problem is that the measurements are subject to very high level of noise due to maneuvering of the vehicle, nonlinear effects of the suspension system, vibration of the engine and the gear system, and sensor noise. Nonparametric and parametric modeling methods are applied to evaluate the quality of the measurements and find the invariant properties of the suspension system. It is shown based on multiple experiments that independently of the actual road properties and operating conditions, eigen-frequencies of some vibration modes can be determined with relatively small uncertainty, while the corresponding damping factors have varying amount of uncertainty. Comparing the results with the modes of a full car vehicle model developed based on physical considerations, it can be concluded that an identification algorithm for obtaining the parameters of the physical model must be complemented with a suitable uncertainty modeling and classification.

Open Access: Yes

DOI: 10.1109/CoDIT.2017.8102707

AR and ARMA spectral analysis of suspension system of a commercial city bus

Gabor Rodonyi György Istenes Ferenc Szauter

Publication Name: Cinti 2016 17th IEEE International Symposium on Computational Intelligence and Informatics Proceedings

Publication Date: 2017-02-07

Volume: Unknown

Issue: Unknown

Page Range: 151-156

Description:

Concerning the increasing demand for intelligent and efficient urban vehicle systems with low cost maintainability and high passenger comfort, reliable methods are needed to model and to evaluate the imposed performances. The measurements, vibrations emerging on the wheels and the body, that has ben taken on a city bus are analyzed in the frequency domain. In this paper a parametric spectral analysis (AR/ARMA method) of the suspension system of a commercial city bus is presented. The goal of the analysis is to find the right structure for the systems. Parametric methods used in this paper justify and extend the results obtained by non-parametric ones and provide more accurate results for vibration analysis. One of the main conclusions of the investigations is that the quarter-car model structure based on first principles does not reflect the true frequency domain behavior of the system. Thus the identification of the physical model must be complemented with a suitable uncertainty modeling and classification.

Open Access: Yes

DOI: 10.1109/CINTI.2016.7846395

Spectral analysis of suspension system of a commercial city bus

Gabor Rodonyi György Istenes Ferenc Szauter

Publication Name: Sisy 2016 IEEE 14th International Symposium on Intelligent Systems and Informatics Proceedings

Publication Date: 2016-10-19

Volume: Unknown

Issue: Unknown

Page Range: 67-72

Description:

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.

Open Access: Yes

DOI: 10.1109/SISY.2016.7601473