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Dynamics and control of rider-bicycle systems

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TitleInfo
Title
Dynamics and control of rider-bicycle systems
Name (type = personal)
NamePart (type = family)
Wang
NamePart (type = given)
Pengcheng
NamePart (type = date)
1985-
DisplayForm
Pengcheng Wang
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Yi
NamePart (type = given)
Jingang
DisplayForm
Jingang Yi
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Advisory Committee
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chair
Name (type = personal)
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Zou
NamePart (type = given)
Qingze
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Qingze Zou
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Advisory Committee
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internal member
Name (type = personal)
NamePart (type = family)
Bai
NamePart (type = given)
Xiaoli
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Xiaoli Bai
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Torres
NamePart (type = given)
Elizabeth
DisplayForm
Elizabeth Torres
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
outside member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
School of Graduate Studies
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (qualifier = exact)
2018
DateOther (qualifier = exact); (type = degree)
2018-10
CopyrightDate (encoding = w3cdtf)
2018
Place
PlaceTerm (type = code)
xx
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
How can an autonomous bicycle robot system keep balance and track a path? How does a human rider ride a bicycle? And how can we enhance human riding safety and efficiency? Answers of these questions can provide guidance for autonomous single-track vehicle control system design, understanding human riding skills and vehicle assistive design. Furthermore, riding a bicycle is an unstable physical human-machine interaction (upHMI). Riding skills analysis is a good example about understanding human control mechanism, including human body movement control and human neuro-control. The bicycle assisted balancing system also provides the inspiration for designing other human-robot cooperation system. This dissertation has three objectives: the first one is to design control system for autonomous bicycle for balancing and tracking; the second one is to model and analyze the human riding skills of balancing and tracking; and the last one is to design tuning method for human riding balancing skills.

The first part of this dissertation focuses on the autonomous bicycle control system design for balancing and path following. The bikebot, an autonomous bicycle system, is designed for these control mechanism implementation. The gyro-balancer control law and steering motion control law are designed for balancing the bikebot system in the stationary and moving stages, respectively. Using these two control laws, a switching control strategy is proposed for a stationary-moving transition process. The control performances are demonstrated by the experimental results for a complete maneuver.

For the trajectory tracking tasks, the external/internal convertible (EIC) structure-based control strategies are proposed and implemented. The EIC-based control takes the advantages of the non-minimum phase underactuated dynamics structure. We first analyze and demonstrate the EIC-based motion tracking controller. An auxiliary gyro subsystem control law is then designed to enhance the tracking performance of the EIC-based controller. The errors dynamics and control properties are discussed and analyzed. Finally, the control strategies are implemented on the bikebot system. The experiments results confirm and demonstrate the controllers effectiveness.

The second part of the dissertation focuses on the analysis of human riding skills, including the balance control and the tracking skills. For the motion tracking with balancing motor skills, using the EIC structure, a balance equilibrium manifold (BEM) concept is proposed for analyzing the human trajectory tracking behaviors and balancing properties. The contributions of steering and upper-body motion are analyzed quantitatively. Finally, performance metrics are introduced to quantify the balance motor skills using the BEM concept. These analysis and discussions are demonstrated and validated by extensive human riding experiments. Comparison between the EIC-based control and human control is also presented and demonstrated.

For the balance skill studies, we first present the control models of human steering angle and upper-body leaning torque. These models are inspired by the human stance balance studies and built on several groups of human riding experiments. The parameters sensitivity analyses are also discussed with experiment validation. Using the time-delayed system stability analysis, the quantitative influences of the model parameters on closed-loop stability are also demonstrated and experimentally verified.

Based on aforementioned results, actively tuning the rider-bikebot interaction is the aim of the last part of the dissertation. First, from the rider-bikebot interaction dynamics, the stiffness and damping effect for balancing are analyzed. The control of the rider-bikebot interactions is designed to tune the stiffness and damping effects by reshaping the rider steering motion. From experiments observation, the rider balancing performances are significantly improved under the tuned interaction dynamics. Furthermore, under a special tuned stiffness and damping effect, the rider-bikebot system can be balanced autonomously without considering the rider steering input. This property is also theoretically proven and also verified by the experiments.

The outcomes of this dissertation not only advances the understanding the human rider balance motor skills but also provides the guidance for the autonomous bicycle control design, and the human balancing performance tuning method through rider-bikebot interactions. At the end of this dissertation, future work directions are also discussed and presented.
Subject (authority = RUETD)
Topic
Mechanical and Aerospace Engineering
Subject (authority = ETD-LCSH)
Topic
Autonomous robots
Subject (authority = ETD-LCSH)
Topic
Cycling
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_9321
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (132 pages : illustrations)
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Pengcheng Wang
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TitleInfo
Title
School of Graduate Studies Electronic Theses and Dissertations
Identifier (type = local)
rucore10001600001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier (type = doi)
doi:10.7282/t3-cmsg-2316
Genre (authority = ExL-Esploro)
ETD doctoral
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RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Wang
GivenName
Pengcheng
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2018-10-02 22:06:32
AssociatedEntity
Name
Pengcheng Wang
Role
Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
AssociatedObject
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License
Name
Author Agreement License
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
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Copyright protected
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Status
Open
Reason
Permission or license
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