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dc.contributor.author
Rey, Felix
dc.contributor.supervisor
Lygeros, John
dc.contributor.supervisor
Hokayem, Peter
dc.contributor.supervisor
Goulart, Paul
dc.contributor.supervisor
Giselsson, Pontus
dc.date.accessioned
2019-01-24T10:04:28Z
dc.date.available
2018-12-18T09:37:16Z
dc.date.available
2019-01-24T09:46:09Z
dc.date.available
2019-01-24T10:04:28Z
dc.date.issued
2018-12
dc.identifier.isbn
978-3-906916-44-6
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/311505
dc.identifier.doi
10.3929/ethz-b-000311505
dc.description.abstract
The alternating direction method of multipliers (ADMM) is a first-order optimization algorithm for solving convex problems. We study ADMM under a specialization paradigm, which means that we shape the algorithm to customize it to the optimization problem at hand. This specialization paradigm comes as a contrast to using an ‘off-the-shelf’ or general-purpose solver, which follows a ‘one size fits all’ policy. We show that algorithm specialization makes it possible to synthesize a range of desirable algorithm features and characteristics, which promotes ADMM as a powerful and versatile tool in optimization and control. We study specialized ADMM in a variety of forms and for a range of applications. We first consider an optimal bidding strategy for energy reserve markets. In such markets, guarantees for providing grid-balancing power capacity are traded. We devise an ADMM-driven negotiation protocol that coordinates aggregations of market participants to place a joint energy reserve bid. We show that this algorithmic negotiation equally distributes the computational burden to the participants and that it provides further beneficial features, such as low set-up costs and a large participant autonomy. In a second specialized ADMM formulation, we contribute to the area of autonomous coordination and collision avoidance. We present a decentralized ADMM-based navigation protocol for the coordination of moving agents. A challenge arises from the nonconvex collision avoidance constraints, which we handle with successive linearization at each ADMM iteration. A main feature of the resulting algorithm is that it operates from an agent’s point of view, which means that each agent uses a local coordinate system and interacts with its nearest neighbors only. We show that this fully decentralized perspective provides a range of favorable properties to handle the coordination task. Besides tailor-fitting ADMM for specific areas, we also propose an ADMM specialization framework for a general class of model predictive control (MPC) problems. More specifically, we devise a method that uses the structure in the controlled system as an additional source of efficiency for the optimization routine. The algorithm then mimics the structural properties of the system, which leads to an improved performance of the overall procedure. The resulting ADMM formulations are highly parallelizable and particularly suited for embedded implementation. We also present a novel measure for system structure, called the separation tendency. With the separation tendency, we can decide whether a system is sufficiently structured for utilizing our ADMM framework. In the last part of this thesis, we provide a detailed application study for structure-exploiting ADMM. We show how to model and control the power flow in a variable speed drive (VSD). It turns out that the resulting VSD model is a structured system, which makes controlling the power flow in the VSD an excellent example for algorithm specialization.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
ETH Zurich
en_US
dc.rights.uri
http://rightsstatements.org/page/InC-NC/1.0/
dc.subject
ADMM
en_US
dc.subject
Optimization algorithms
en_US
dc.subject
Distributed algorithms
en_US
dc.subject
Model predictive control
en_US
dc.title
ADMM in Optimization and Control
en_US
dc.type
Doctoral Thesis
dc.rights.license
In Copyright - Non-Commercial Use Permitted
dc.date.published
2019-01-24
ethz.title.subtitle
Algorithm Specialization, Computational Distribution, and the Value of Structure
en_US
ethz.size
166 p.
en_US
ethz.code.ddc
DDC - DDC::0 - Computer science, information & general works::004 - Data processing, computer science
ethz.code.ddc
DDC - DDC::5 - Science::510 - Mathematics
ethz.identifier.diss
25641
en_US
ethz.publication.place
Zurich
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02650 - Institut für Automatik / Automatic Control Laboratory::03751 - Lygeros, John / Lygeros, John
en_US
ethz.date.deposited
2018-12-18T09:37:28Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2019-01-24T10:04:44Z
ethz.rosetta.lastUpdated
2021-02-15T03:25:50Z
ethz.rosetta.versionExported
true
ethz.COinS
ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.atitle=ADMM%20in%20Optimization%20and%20Control&rft.date=2018-12&rft.au=Rey,%20Felix&rft.isbn=978-3-906916-44-6&rft.genre=unknown&rft.btitle=ADMM%20in%20Optimization%20and%20Control
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