Implementation of Motion Cueing and Motor Position Control for Vehicle Simulator with 4-DOF-Platform

Aulia, Achmad Indra; Fahmi, Monika Faswia; Hindersah, Hilwadi; Rohman, Arief Syaichu; Hidayat, Egi

doi:10.1109/icevt48285.2019.8994028

Cited by 5 publications

(1 citation statement)

References 7 publications

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“…Sliding mode control and model predictive control were studied on how to drive the motion cueing algorithm (MCA) to the system on the 4 DoF driving platform. Evaluations were made on pitch, roll, sway, and surge, and it was observed that the sliding mode control has a saturation function and the model predictive controller (MPC) gave better results [26]. Due to the high financial requirements of high-fidelity driving simulator within the scope of human machine interaction, a platform was studied on a mediumfidelity flexible and reusable platform that can provide testing of automotive components by increasing the flexibility of existing systems [27].…”

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 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: Introductionmentioning

confidence: 99%

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

et al. 2023

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Ultrasonic Signal Implementation in Arduino-Based Obstacle Robot Control System

2021

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The results of the distance calculation used to control the movement of the robot, hanse the robot is able to avoid unknown obstacles. This obstacle robot divided into 3 parts, namely Arduino Uno as a controller, L298N driver as a motor/wheel controller and ultrasonic sensor HC-SR04 as a sending and receiving device for ultrasonic signals. The ultrasonic sensor design on the obstacle robot placed at the front of the robot with the obstacle position in front. From the data analysis, the obstacle robot can determine the accuracy level of the detected distance and can stop according to the detected obstacle distance. The test results show that the obstacle robot is less accurate in detecting the obstacles in front of it, as evidenced by the test results that there is an average error of 0.118. The obstacle robot made using an ultrasonic sensor, so it is less accurate in reading the presence of a obstacle in front of it. In the test results, the biggest error is when the robot is at a distance of 30 cm, where the robot stops at a distance of 24 cm so that there is an error of 6 cm that missed.

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Comparison Of Voltage Measurements on DC Gearbox Motor and PWM Voltage Based On Arduino Uno

Ulum

Laksono

2021

E3S Web Conf.

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In this research, a DC motor tested using the L298 motor driver which is controlled by Arduino as the motor speed regulator and using the PWM method as a speed control signal generator for the DC motor. This research has several stages, namely literature study, arduino-based dc motor design, Arduino-based dc motor production, Arduino-based dc motor testing, then analysis of research results and conclusions are drawn. Testing on this Arduino-bas, ed DC motor was done with several experiments, by adjust the motor speed by setting the pwm value in the program listing section then measuring the pwm output on pin 9 of the Arduino Uno board. Then measurements were made on the right and left side of the motor. This experiment was carried out by setting the pwm value from 15 to 255 to determine the difference in the increase in the PWM output voltage and determine the voltage on the motor and determine the condition of the DC motor. The test results show that the initial motor moves when the set value of PWM = 45, PWM voltage = 0.83 V, right motor voltage = 1.53 V, and left motor = 1.75 V.

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Implementation of Motion Cueing and Motor Position Control for Vehicle Simulator with 4-DOF-Platform

Cited by 5 publications

References 7 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

Ultrasonic Signal Implementation in Arduino-Based Obstacle Robot Control System

Comparison Of Voltage Measurements on DC Gearbox Motor and PWM Voltage Based On Arduino Uno

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