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Dynamic Control of Formula: Towards Driverless.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Dynamic Control of Formula: Towards Driverless./
Author:	Lu, Jing.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	33 p.
Notes:	Source: Masters Abstracts International, Volume: 82-08.
Contained By:	Masters Abstracts International82-08.
Subject:	Mechanical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28263737
ISBN:	9798569995424

Dynamic Control of Formula: Towards Driverless.
Lu, Jing.

Dynamic Control of Formula: Towards Driverless. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 33 p.

Source: Masters Abstracts International, Volume: 82-08.

Thesis (Master's)--University of Washington, 2020.

This item must not be sold to any third party vendors.

This paper presents vehicle dynamic control algorithms for a dual-motor RWD electric racing car of UW Formula Motorsports. Team-31 vehicle was designed for human driver only, so the team proposed an PID (proportional-integral-derivative) based yaw rate and slip ratio control on dynamic bicycle or four-wheel vehicle dynamics model. The algorithm aimed to improve acceleration behavior in straight line event and cornering maneuverability. It will also serve as the low-level stability control for the future autonomous racing car.Team-32 aimed to compete in FSG driverless events in 2022~2023, so the control system should merge into autonomous driving hardware platform with a MPC (model predictive control) based trajectory tracking algorithm. Vehicle behavior and control effectiveness were analyzed using MATLAB Simulink, and hopefully would be validated in HIL (Hardware-in-loop) setup or RC car in the future.In Section 2: Vehicle handling and performance, the author explained the derivation of vehicle lateral and longitudinal dynamics model, critical parameters for cornering behavior evaluation and their effects on turn stability. Analysis of a traditional torque vectoring and traction control strategy was explained under steady states condition. In section 3: Model predictive control for autonomous driving, an optimization-based trajectory tracking method was introduced based on kinematic vehicle model. The controller behavior was evaluated under simulated competition events. Section 4: Hardware implementation covered key points in modeling and configurating critical actuators and sensors for vehicle test preparation.

ISBN: 9798569995424Subjects--Topical Terms:

649730
Mechanical engineering.
Subjects--Index Terms:

Autonomous

Dynamic Control of Formula: Towards Driverless.
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245 1 0 $a Dynamic Control of Formula: Towards Driverless.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 33 p.
500 $a Source: Masters Abstracts International, Volume: 82-08.
500 $a Advisor: Emery, Ashley F.
502 $a Thesis (Master's)--University of Washington, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a This paper presents vehicle dynamic control algorithms for a dual-motor RWD electric racing car of UW Formula Motorsports. Team-31 vehicle was designed for human driver only, so the team proposed an PID (proportional-integral-derivative) based yaw rate and slip ratio control on dynamic bicycle or four-wheel vehicle dynamics model. The algorithm aimed to improve acceleration behavior in straight line event and cornering maneuverability. It will also serve as the low-level stability control for the future autonomous racing car.Team-32 aimed to compete in FSG driverless events in 2022~2023, so the control system should merge into autonomous driving hardware platform with a MPC (model predictive control) based trajectory tracking algorithm. Vehicle behavior and control effectiveness were analyzed using MATLAB Simulink, and hopefully would be validated in HIL (Hardware-in-loop) setup or RC car in the future.In Section 2: Vehicle handling and performance, the author explained the derivation of vehicle lateral and longitudinal dynamics model, critical parameters for cornering behavior evaluation and their effects on turn stability. Analysis of a traditional torque vectoring and traction control strategy was explained under steady states condition. In section 3: Model predictive control for autonomous driving, an optimization-based trajectory tracking method was introduced based on kinematic vehicle model. The controller behavior was evaluated under simulated competition events. Section 4: Hardware implementation covered key points in modeling and configurating critical actuators and sensors for vehicle test preparation.
590 $a School code: 0250.
650 4 $a Mechanical engineering. $3 649730
650 4 $a Automotive engineering. $3 2181195
650 4 $a Systems science. $3 3168411
653 $a Autonomous
653 $a Driverless
653 $a Formula
653 $a Torque vectoring
653 $a Traction control
653 $a Vehicle dynamics
690 $a 0548
690 $a 0540
690 $a 0790
710 2 $a University of Washington. $b Mechanical Engineering. $3 2092993
773 0 $t Masters Abstracts International $g 82-08.
790 $a 0250
791 $a Master's
792 $a 2020
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28263737