Vehicle stability controller HiL validation on static simulator

Alfatti, F; Annicchiarico, Claudio; Capitani, Renzo

doi:10.1088/1757-899x/1214/1/012044

Cited by 6 publications

(4 citation statements)

References 8 publications

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“…The unit functionalities have been tested in [41], comparing the responses and behaviors on the static simulator with respect to a real vehicle, proving its effectiveness on a previously validated vehicle model.…”

Section: Braking Unitmentioning

confidence: 96%

Implementation and Performances Evaluation of Advanced Automotive Lateral Stability Controls on a Real-Time Hardware in the Loop Driving Simulator

et al. 2023

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This study concerns the comparative investigation of two advanced lateral stability automotive controllers with respect to a commercial solution. The research aims to improve the stability performances achieved by a combined tracking of yaw rate and side-slip angle through the application of optimal efforts. The proposed solutions are based on Linear Quadratic Regulation and Sliding Mode Control, respectively. Both rely on the same approach for the control objective definition but differ from the action perspective. This solution involves the adoption of a differential braking actuation technique to deliver a desired yaw moment to the car body to track controlled states. Indeed, a sliding controller can also traction torques of hub-motor configurations as well as steering corrections, achieving vehicle stability and a driving response in accordance with the pilot’s intentions. Calibration and validation of the controllers are performed through a Hardware-in-the-Loop simulation rig, along with a real-time static simulator, performing different close-loop maneuvers to assess achievements in terms of lateral stability. Results show that both solutions ensure higher handling performances if compared to Non-controlled or Commercial-controlled vehicle scenarios.

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Section: Braking Unitmentioning

confidence: 96%

Implementation and Performances Evaluation of Advanced Automotive Lateral Stability Controls on a Real-Time Hardware in the Loop Driving Simulator

et al. 2023

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“…The brake bench has been installed on the driving simulator thanks to the collaboration with the company Meccanica 42. The simulator setup is the same as reported in [6,11,12,13]: for the scope of this work, the simulator can be described as composed by four main parts: the driver cockpit, the real time simulator, the EPSiL steering bench, and the Braking bench. With this specific setup, the driver can then test both steering and brake unit hardware, equipped with stock ADAS functionalities.…”

Section: Real-time Driving Simulator Setupmentioning

confidence: 99%

Hardware in the Loop Methodology for AEB system development

Alfatti

Annicchiarico²,

Capitani

2023

IOP Conf. Ser.: Mater. Sci. Eng.

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Thanks to ADAS systems, vehicle and occupants safety has increased. These systems have however a great impact on design complexity, increasing the time necessary for the launch of the functionality on the market. In this work, a Hardware in the Loop tool and methodology that integrates the Autonomous Emergency System (AEB) is presented, with the objective to speed up the development process. A commercial brake-by-wire braking system has been installed on a static simulator. The unit stock AEB can be activated during the real-time simulation, thanks to a custom strategy reproduced via Software in the Loop approach. In order to assess the capabilities of the proposed methodology, two different controllers have been proposed: the first reproduces the AEB functionality of a commercial car, while the second represent a possible design alternative that must be evaluated during the design phase. The two controllers have been tested in EuroNCAP relevant scenarios, and the performances have been compared in terms of Time To Collision necessary to avoid the impact, deceleration request and final distance from the obstacle. The proposed methodology highlights the role of the static simulator as a development tool that can vastly reduce the time necessary for the development of the AEB system, thanks to an extensive testing of both hardware and control strategy.

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“…Yin et al [22] proposed a system to predict the pedal feel at the time of braking. The pressure changes inside the braking system lead to changes in the feeling of the braking pedal [23]. To overcome this problem, the pedal feel is studied in relation to pressure changes to stabilize those changes by applying a fully decoupled brake-by-wire system [18], [23].…”

Section: Introductionmentioning

confidence: 99%

“…The pressure changes inside the braking system lead to changes in the feeling of the braking pedal [23]. To overcome this problem, the pedal feel is studied in relation to pressure changes to stabilize those changes by applying a fully decoupled brake-by-wire system [18], [23]. Mullner et al [24] suggested a Simulink model that would be used as the last verification step before applying any proposed system.…”

Section: Introductionmentioning

confidence: 99%

Design and analysis of a new brake-by-wire system using machine learning

Hassanein

Dawod

Hassan

2023

IJECE

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One of the main aims of the recent research on brake-by-wire systems is to decrease mechanical components. In this paper, we propose replacing the brake pedal with a driving wheel that is fully covered by pressure braking batch sensors. The new mechanism for braking translates pressure exerted through the driver’s hands on the driving wheel to a corresponding electrical signal. A proposed design for the pressure braking batch (PBB) is made out of a mesh of conducting threads separated by a resistive sheet. To the best of our knowledge, this idea has not been raised before in other research papers. Different people have different muscle strengths and so the problem of identifying the intention of the user when pressing the PBB is tackled. For this aim, a new dataset of its kind is created by several volunteers. From each volunteer, age, gender, body mass index (BMI), and maximum pressure exerted on the driving wheel are collected. Using Weka software, the detection accuracy is calculated for a new volunteer to know the intention of his/her pressure on PBB. Among the three algorithms tried, the regression tree gives the best results in predicting the class of the pressure exerted by the volunteers.

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Vehicle stability controller HiL validation on static simulator

Cited by 6 publications

References 8 publications

Implementation and Performances Evaluation of Advanced Automotive Lateral Stability Controls on a Real-Time Hardware in the Loop Driving Simulator

Implementation and Performances Evaluation of Advanced Automotive Lateral Stability Controls on a Real-Time Hardware in the Loop Driving Simulator

Hardware in the Loop Methodology for AEB system development

Design and analysis of a new brake-by-wire system using machine learning

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