scholarly journals Lateral Vehicle Control for Semi-Autonomous Valet Parking with Consideration of Actuator Dynamics

2013 ◽  
Vol 2 (6) ◽  
pp. 150-156 ◽  
Author(s):  
Dohyun Kim ◽  
Bongsob Song
Keyword(s):  
Vehicle Control ◽  
Actuator Dynamics

Assessing the validity of the ride motion simulator for a remote vehicle control task

2005 ◽  
Author(s):  
John W. Ruffner ◽  
Kaleb McDowell ◽  
Victor J. Paul ◽  
Harry J. Zywiol ◽  
Todd T. Mortsfield ◽  
...  
Keyword(s):  
Vehicle Control ◽  
Control Task ◽  
Motion Simulator

Auditory-Visual Redundancy in Vehicle Control Interruptions: Two Meta-analyses

2011 ◽  
Author(s):  
Christopher Wickens ◽  
Julie Prinet ◽  
Shaun Hutchins ◽  
Nadine Sarter ◽  
Angelia Sebok
Keyword(s):  
Vehicle Control ◽  
Meta Analyses

Effect of Inner and Outer Wheels Driving Force Control on Small Electric Vehicle

2020 ◽  
Vol 17 (4) ◽  
pp. 8246-8254
Author(s):  
K. Shibazaki ◽  
H. Nozaki
Keyword(s):  
Control System ◽  
Angular Velocity ◽  
Electric Vehicle ◽  
Force Constant ◽  
Force Control ◽  
Driving Force ◽  
Vehicle Control ◽  
Steering Angle ◽  
Force Control System ◽  
The Difference

In this study, in order to improve steering stability during turning, we devised an inner and outer wheel driving force control system that is based on the steering angle and steering angular velocity, and verified its effectiveness via running tests. In the driving force control system based on steering angle, the inner wheel driving force is weakened in proportion to the steering angle during a turn, and the difference in driving force is applied to the inner and outer wheels by strengthening the outer wheel driving force. In the driving force control (based on steering angular velocity), the value obtained by multiplying the driving force constant and the steering angular velocity,  that differentiates the driver steering input during turning output as the driving force of the inner and outer wheels. By controlling the driving force of the inner and outer wheels, it reduces the maximum steering angle by 40 deg and it became possible to improve the cornering marginal performance and improve the steering stability at the J-turn. In the pylon slalom it reduces the maximum steering angle by 45 deg and it became possible to improve the responsiveness of the vehicle. Control by steering angle is effective during steady turning, while control by steering angular velocity is effective during sharp turning. The inner and outer wheel driving force control are expected to further improve steering stability.

Vehicle Control Unit (VCU) for the HMMWV

2001 ◽  
Author(s):  
CALIFORNIA UNIV LOS ANGELES
Keyword(s):  
Control Unit ◽  
Vehicle Control

scholarly journals An Online Evolving Framework for Advancing Reinforcement-Learning based Automated Vehicle Control

2020 ◽  
Vol 53 (2) ◽  
pp. 8118-8123
Author(s):  
Teawon Han ◽  
Subramanya Nageshrao ◽  
Dimitar P. Filev ◽  
Ümit Özgüner
Keyword(s):  
Reinforcement Learning ◽  
Vehicle Control ◽  
Automated Vehicle ◽  
Automated Vehicle Control

scholarly journals Sensing, Actuation, and Control of the SmartX Prototype Morphing Wing in the Wind Tunnel

Actuators ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 107
Author(s):  
Nakash Nazeer ◽  
Xuerui Wang ◽  
Roger M. Groves
Keyword(s):  
Wind Tunnel ◽  
Optical Fibers ◽  
Fiber Optic ◽  
Experimental Testing ◽  
Single Mode ◽  
Fiber Optic Sensor ◽  
Sensor Data ◽  
Random Errors ◽  
Morphing Wing ◽  
Actuator Dynamics

This paper presents a study on trailing edge deflection estimation for the SmartX camber morphing wing demonstrator. This demonstrator integrates the technologies of smart sensing, smart actuation and smart controls using a six module distributed morphing concept. The morphing sequence is brought about by two actuators present at both ends of each of the morphing modules. The deflection estimation is carried out by interrogating optical fibers that are bonded on to the wing’s inner surface. A novel application is demonstrated using this method that utilizes the least amount of sensors for load monitoring purposes. The fiber optic sensor data is used to measure the deflections of the modules in the wind tunnel using a multi-modal fiber optic sensing approach and is compared to the deflections estimated by the actuators. Each module is probed by single-mode optical fibers that contain just four grating sensors and consider both bending and torsional deformations. The fiber optic method in this work combines the principles of hybrid interferometry and FBG spectral sensing. The analysis involves an initial calibration procedure outside the wind tunnel followed by experimental testing in the wind tunnel. This method is shown to experimentally achieve an accuracy of 2.8 mm deflection with an error of 9%. The error sources, including actuator dynamics, random errors, and nonlinear mechanical backlash, are identified and discussed.

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