2020
DOI: 10.3390/vehicles2040036
|View full text |Cite
|
Sign up to set email alerts
|

MPC-Based Motion-Cueing Algorithm for a 6-DOF Driving Simulator with Actuator Constraints

Abstract: Driving simulators are widely used for understanding human–machine interaction, driver behavior and in driver training. The effectiveness of simulators in this process depends largely on their ability to generate realistic motion cues. Though the conventional filter-based motion-cueing strategies have provided reasonable results, these methods suffer from poor workspace management. To address this issue, linear MPC-based strategies have been applied in the past. However, since the kinematics of the motion plat… Show more

Help me understand this report
View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1

Citation Types

0
7
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
8
2

Relationship

2
8

Authors

Journals

citations
Cited by 26 publications
(7 citation statements)
references
References 29 publications
0
7
0
Order By: Relevance
“…The communication operates at 20 Hz, identical to the controller's sampling frequency, while the vehicle dynamics are updated at 1 kHz. Also included in the HIL setup is the Delft Advanced Driving Simulator (DAVSi) [31], which mainly consists of a mock-up of the front half of a Toyota Yaris and a hexapod motion platform driven by six linear motors. The experiment runs in hard real-time mode, where if the turnaround time of the controller exceeds the sampling time, the simulation is terminated immediately.…”
Section: Hil Setupmentioning
confidence: 99%
“…The communication operates at 20 Hz, identical to the controller's sampling frequency, while the vehicle dynamics are updated at 1 kHz. Also included in the HIL setup is the Delft Advanced Driving Simulator (DAVSi) [31], which mainly consists of a mock-up of the front half of a Toyota Yaris and a hexapod motion platform driven by six linear motors. The experiment runs in hard real-time mode, where if the turnaround time of the controller exceeds the sampling time, the simulation is terminated immediately.…”
Section: Hil Setupmentioning
confidence: 99%
“…Javier Velasco [ 26 ] improved the performance and accuracy of a positioning system based on the Stewart platform by proposing a sliding mode control strategy for accurate positioning, and this control scheme provides a reference for application to larger motion platforms and complex simulation robots. In the research and development of a driving simulator, Yash Raj Khusro [ 27 ] designed a 6-DOF simulator with actuator constraints based on the MPC motion hint algorithm, which improved the accuracy of the 6-DOF motion platform under nonlinear motion conditions. Cosmin Copot [ 28 ] designed a 6-DOF motion platform for realizing virtual environment space docking operation equipment under manual monitoring.…”
Section: Introductionmentioning
confidence: 99%
“…To enhance simulator fidelity, efforts have been made in this research field to provide a more realistic sense of presence [ 14 ]. Thus, advanced artificial intelligence (AI) techniques, including deep neural network (DNN) [ 15 ], fuzzy logic [ 16 , 17 , 18 ], or genetic algorithm [ 19 ], have been exploited to optimize platform motion cueing in a high degrees-of-freedom (DOF) in the roll, pitch, and yaw axis. However, other studies underlined the high-cost issue of such developed software and hardware of motion platforms and intended to reduce simulators’ cost by decreasing the freedom to 3-DOF [ 20 ], 2-DOF [ 21 ], or even to completely static simulators [ 22 , 23 , 24 , 25 , 26 ].…”
Section: Introductionmentioning
confidence: 99%