High Speed Emulation in a Vehicle-in-the-Loop Driving Simulator

Weiss, Elliot; Gerdes, J. Christian

doi:10.1109/tiv.2022.3162549

Cited by 3 publications

(4 citation statements)

References 27 publications

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“…According to the design constraint, structure and parameter, a BBD is performed. In BBD, the hidden layer neuron number (40,60,80), training algorithm (Bayesian Regularization, Scaled Conjugate Gradient, Levenberg-Marquardt) and NN structure (3 in-2 out, 6 in-2 out, 6 in-4 out) as the design parameters are determined for BBD. After the BBD, NN performance, MSE and Processing Cycle are observed as results of the experiments.…”

Section: Dof Vehicle Simulatormentioning

confidence: 99%

“…It has been studied to develop design and control algorithms on the light air bearing simulator [39]. A simulator with Vehicle in the loop was studied for a realistic driving feeling with haptic steering feedback in high speed applications [40]. A motion cueing algorithm-based model predictive controller has been studied for the hexapod with 9 DoF, which exhibits nonlinear behavior [41].…”

Section: Introductionmentioning

confidence: 99%

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Feature Extraction and NN-Based Enhanced Test Maneuver Deployment for 2 DoF Vehicle Simulator

et al. 2023

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This paper presents a deployment method of various test maneuver scenarios for 2 degree of freedom (2 DoF) vehicle simulator by using feature extraction and neural networks (NN). A prototype version has been set up for the 2 DoF vehicle simulator. Then, a hardware in the loop (HIL) model with 2 inputs (torque, τ 1 -τ 2 ) and 3 outputs (acceleration, a x -a y -a z ) is created. System identification is performed to obtain the training data of NNs to be used for the deployment of test maneuvers. In the system identification process, 2 arbitrary sinusoidal torque signals (τ 1 -τ 2 ) are generated by using the actuator specs of the 2 DoF vehicle simulator. By applying the generated torque signals to the actuators, acceleration (a x -a y -a z ) data are collected from the inertial measurement sensor (IMU) on the 2 DoF vehicle simulator. It is determined to create 3 different NN models for the obtained data. The 1st NN model is trained with 3 inputs (a x -a y -a z ) and 2 targets (τ 1 -τ 2 ) training data. The 2nd NN model is trained with 6 inputs (amplitudes and phases of a x -a y -a z ) and 2 targets (τ 1 -τ 2 ) training data. The input data features for the 2nd NN model is extracted by using the Fast Fourier Transform (FFT). The 3rd NN model is trained with 6 inputs (amplitudes and phases of a x -a y -a z ) and 4 targets (amplitudes and phases of τ 1 -τ 2 ) training data. For the 3rd NN model, the features of input and target data are extracted by using the FFT. The NN training process continues until acceptable performance criteria are reached. Then, 3 NN models are run and analyzed under various test scenarios such as Double Lane Change, Constant Radius, Increase Steer, Fish Hook, Sine with Dwell and Swept Sine. Only for the 3rd NN, the actuator signals (τ 1 -τ 2 ) are recomposed by applying an inverse FFT process to the 4 targets (amplitudes and phases of τ 1 -τ 2 ). Finally, the reference trajectory tracking performances are evaluated by comparing the NN models that are run under the test scenarios.

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Section: Dof Vehicle Simulatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Feature Extraction and NN-Based Enhanced Test Maneuver Deployment for 2 DoF Vehicle Simulator

et al. 2023

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“…This allows the transfer of driving sensations such as maneuvering and acceleration to the driver, providing a realistic driving experience. These simulators can be used to measure the dynamic responses of the driver [1][2][3][4][5]. Linear actuators are typically used to connect the vehicle simulator to appropriate configuration, allowing the vehicle simulator to be considered as a parallel manipulator.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, the brackets of a 2 degree-of-249 freedom (2 DoF) vehicle simulator are considered in the context of enhanced topology optimization with driving scenarios at a multisystem level. The mathematical model with an integrated kinematic and dynamic for the 2 DoF vehicle simulator is represented between Equation (1) to (5). The Lagrangian formulation, instead of to using the force and moments of the individual components, characterizes the behavior of a dynamic system in terms of work and energy stored in the system.…”

Section: Introductionmentioning

confidence: 99%

Multi System Level Driving Scenarious Based Topology Optimization of Bracket Design for 2 DoF Vehicle Simulator

DEMİRCİ,

DEMİR,

AKGÜN

et al. 2023

International Journal of Automotive Science and Technology

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This article presents a topology optimization of the motor mounting bracket in a 2-degree-of-freedom (2 DoF) vehicle simulator that is enhanced by the driving scenari-os. Firstly, a 14 DoF passenger reference application model is determined in the Sim-ulink environment. Then, common driving scenarios (Constant Radius, Double Lane Change, Fishhook, Increasing Steer, Sine with Dwell and Swept Sine) are run on 14 DoF vehicle models to test the dynamic performance of the vehicle. During the anal-ysis, accelerations in the XYZ axes are logged, and the minimum and maximum ac-celeration values on each axis are grouped separately for each driving scenario. Next, the concept design of 2 DoF vehicle simulators is created. The obtained accelerations from the driving scenarios are then run on 2 DoF vehicle simulator in the Solidworks simulation environment, and stress and deformation on the 2 DoF vehicle simulator are analyzed. During this analysis, linear actuator and axis forces are calculated ac-cording to the reaction forces on the vehicle simulator. Under the determined axial forces, the brackets are subjected to topology optimization. The obtained generative design of the bracket is reshaped by post-processing for sustainable production. The shape-optimized bracket is run again on the 2 DoF vehicle simulator with the ob-tained acceleration values from the driving scenarios, and the study is completed by performing stress and deformation analysis.

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Research and Development on Noise, Vibration, and Harshness of Road Vehicles Using Driving Simulators—A Review

Xue,

Previati,

Gobbi

et al. 2023

SAE Int. J. Veh. Dyn., Stab., and NVH

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<div>Noise, vibration, and harshness (NVH) is a key aspect in the vehicle development. Reducing noise and vibration to create a comfortable environment is one of the main objectives in vehicle design. In the literature, many theoretical and experimental methods have been presented for improving the NVH performances of vehicles. However, in the great majority of situations, physical prototypes are still required as NVH is highly dependent on subjective human perception and a pure computational approach often does not suffice. In this article, driving simulators are discussed as a tool to reduce the need of physical prototypes allowing a reduction in development time while providing a deep understanding of vehicle NVH characteristics. The present article provides a review of the current development of driving simulator focused on problems, challenges, and solutions for NVH applications. Starting from the definition of the human response to noise and vibration, this article describes the different driving simulator technologies to tackle all the involved perception aspects. The different available technologies are discussed and compared as to provide design engineers with a complete picture of the current possibilities and future trends.</div>

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High Speed Emulation in a Vehicle-in-the-Loop Driving Simulator

Cited by 3 publications

References 27 publications

Feature Extraction and NN-Based Enhanced Test Maneuver Deployment for 2 DoF Vehicle Simulator

Feature Extraction and NN-Based Enhanced Test Maneuver Deployment for 2 DoF Vehicle Simulator

Multi System Level Driving Scenarious Based Topology Optimization of Bracket Design for 2 DoF Vehicle Simulator

Research and Development on Noise, Vibration, and Harshness of Road Vehicles Using Driving Simulators—A Review

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