Validation of a Real-Time Multibody Model for an X-by-Wire Vehicle Prototype Through Field Testing

Author(s):  
Roland Pastorino ◽  
Emilio Sanjurjo ◽  
Alberto Luaces ◽  
Miguel A. Naya ◽  
Wim Desmet ◽  
...  

This research focuses on the experimental validation of a real-time vehicle multibody (MB) model whose bodies are considered rigid. For this purpose, a vehicle prototype has been built and automated in order to repeat reference maneuvers. Numerous sensors on the prototype gather the most relevant magnitudes of the vehicle motion. Two low speed maneuvers involving the longitudinal and lateral vehicle dynamics have been repeated multiple times in a test area. Then, a real-time MB model of the vehicle prototype has been self-developed as well as a simulation environment that includes a true graphical environment, a true road profile, and collision detection. Subsystems like brakes and tires have also been modeled. Both test maneuvers have been simulated with the MB model in the simulation environment using inputs measured experimentally. Selected simulation variables have been compared to their experimental counterparts provided with a confidence interval (IC) that characterizes the field testing (FT) process errors. The results of the comparisons show good correlation between simulation predictions and experimental data, thus allowing to extract useful guidelines to build accurate real-time vehicle MB models. In this way, the present work aims to contribute to the scarce literature on vehicle complete validation studies.

Author(s):  
Isabel Ramirez Ruiz ◽  
Edoardo Sabbioni ◽  
Francesco Braghin ◽  
Federico Cheli

The challenge to enhance the vehicle driving and handling with a state estimation and prediction system is presented by fusing a primary real time multibody vehicle model capable of providing a good indication of vehicle stability and control, and a secondary model able to estimate the vehicle state from vehicle real and virtual sensors to correct the indications of the primary model. A mathematical algorithm combines these two models in the drive control system improving the behavior of the active systems of the vehicle. A Multibody vehicle model has been used to achieve a high fidelity simulation of vehicle dynamics. The selected software is LMS.Virtual.Lab Motion with Real-Time Solver which complements the AMESim Real-Time Solver to handle complex real-time 3D-1D mechatronic systems without any simplified conceptual models. A Sensor Signal Processing Model has been developed to estimate the vehicle states and calculating tire-road contact forces and vehicle sideslip angle. The methodological approach uses the equations of motion of the chassis applying the fundamental principles of classical physics: Newtonian method and Euler angles. The control logic is based on the continuous updating of the preview multibody vehicle model by the controller sensors information network, which makes the model forecast behavior closer to the real one and improve comfort and linearity of the vehicle response. The driver inputs (throttle, steer angle and torque, brake, gear) are the same for the MBS real time model and for the real vehicle. A first training logic updates the MBS model based on the real vehicle behavior calculated by the sensor network, where the logic has to update in the MBS model just the factors depending on the vehicle itself (for example car weight, tire temperature, shock absorber damping forces, tires characteristics) and to understand and keep into account different environment variation (wet / dry surface). If the real vehicle is equipped with active control systems to improve handling and stability, as active camber control, drive by wire, ESP, Body movement active controls, the real time multibody model will interact with the models 1D or 3D of these vehicle dynamics controls and will improve their performance with a very high accuracy prediction of their influence on vehicle dynamic response. In conclusion with the help of the preview multibody vehicle model the drive control logic will increase the performance and drive ability of the vehicle with smart logic interacting with all the active systems.


2016 ◽  
Vol 821 ◽  
pp. 242-247
Author(s):  
Pavel Kučera ◽  
Václav Píštěk

The paper deals with the description of a computational model of an ATV vehicle in Simulink software. This computational model is created using the basic elements of own library and represents the main driveline parts for the simulation of longitudinal and lateral vehicle dynamics. The article describes the design and function of the computational model and demonstrates the usage of an ATV model in the NI VeriStand software. It can be simulated on the hardware for real-time testing. A simulation of drive is then done to verify the functionality of the assembled model.


Robotics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 12
Author(s):  
Yixiang Lim ◽  
Nichakorn Pongsarkornsathien ◽  
Alessandro Gardi ◽  
Roberto Sabatini ◽  
Trevor Kistan ◽  
...  

Advances in unmanned aircraft systems (UAS) have paved the way for progressively higher levels of intelligence and autonomy, supporting new modes of operation, such as the one-to-many (OTM) concept, where a single human operator is responsible for monitoring and coordinating the tasks of multiple unmanned aerial vehicles (UAVs). This paper presents the development and evaluation of cognitive human-machine interfaces and interactions (CHMI2) supporting adaptive automation in OTM applications. A CHMI2 system comprises a network of neurophysiological sensors and machine-learning based models for inferring user cognitive states, as well as the adaptation engine containing a set of transition logics for control/display functions and discrete autonomy levels. Models of the user’s cognitive states are trained on past performance and neurophysiological data during an offline calibration phase, and subsequently used in the online adaptation phase for real-time inference of these cognitive states. To investigate adaptive automation in OTM applications, a scenario involving bushfire detection was developed where a single human operator is responsible for tasking multiple UAV platforms to search for and localize bushfires over a wide area. We present the architecture and design of the UAS simulation environment that was developed, together with various human-machine interface (HMI) formats and functions, to evaluate the CHMI2 system’s feasibility through human-in-the-loop (HITL) experiments. The CHMI2 module was subsequently integrated into the simulation environment, providing the sensing, inference, and adaptation capabilities needed to realise adaptive automation. HITL experiments were performed to verify the CHMI2 module’s functionalities in the offline calibration and online adaptation phases. In particular, results from the online adaptation phase showed that the system was able to support real-time inference and human-machine interface and interaction (HMI2) adaptation. However, the accuracy of the inferred workload was variable across the different participants (with a root mean squared error (RMSE) ranging from 0.2 to 0.6), partly due to the reduced number of neurophysiological features available as real-time inputs and also due to limited training stages in the offline calibration phase. To improve the performance of the system, future work will investigate the use of alternative machine learning techniques, additional neurophysiological input features, and a more extensive training stage.


IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 40706-40715
Author(s):  
Mohammad Reza Satouri ◽  
Abolhassan Razminia ◽  
Saleh Mobayen ◽  
Pawel Skruch

2021 ◽  
Author(s):  
Angelo Domenico Vella ◽  
Antonio Tota ◽  
Alessandro Vigliani

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Longhai Yang ◽  
Xiqiao Zhang ◽  
Jiekun Gong ◽  
Juntao Liu

This paper is concerned with the effect of real-time maximum deceleration in car-following. The real-time maximum acceleration is estimated with vehicle dynamics. It is known that an intelligent driver model (IDM) can control adaptive cruise control (ACC) well. The disadvantages of IDM at high and constant speed are analyzed. A new car-following model which is applied to ACC is established accordingly to modify the desired minimum gap and structure of the IDM. We simulated the new car-following model and IDM under two different kinds of road conditions. In the first, the vehicles drive on a single road, taking dry asphalt road as the example in this paper. In the second, vehicles drive onto a different road, and this paper analyzed the situation in which vehicles drive from a dry asphalt road onto an icy road. From the simulation, we found that the new car-following model can not only ensure driving security and comfort but also control the steady driving of the vehicle with a smaller time headway than IDM.


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