Operator theory in dynamical network systems

gráficas, ilustraciones, tablas

Autores:
Téllez Castro, Duván Andrés
Tipo de recurso:
Doctoral thesis
Fecha de publicación:
2022
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
eng
OAI Identifier:
oai:repositorio.unal.edu.co:unal/82146
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/82146
https://repositorio.unal.edu.co/
Palabra clave:
000 - Ciencias de la computación, información y obras generales
620 - Ingeniería y operaciones afines
Procesamiento de datos
Análisis de sistemas
Ecuaciones
Data processing
System analysis
Equations
Data-Driven Control
Koopman Operator
Optimization
Control con datos
Operador de koopman
optimización
Rights
openAccess
License
Atribución-SinDerivadas 4.0 Internacional
id UNACIONAL2_0bb5eca8a0bc0a3911ceb3de22d14a14
oai_identifier_str oai:repositorio.unal.edu.co:unal/82146
network_acronym_str UNACIONAL2
network_name_str Universidad Nacional de Colombia
repository_id_str
dc.title.eng.fl_str_mv Operator theory in dynamical network systems
dc.title.translated.spa.fl_str_mv Teoría de operadores en sistemas dinámicos en red
title Operator theory in dynamical network systems
spellingShingle Operator theory in dynamical network systems
000 - Ciencias de la computación, información y obras generales
620 - Ingeniería y operaciones afines
Procesamiento de datos
Análisis de sistemas
Ecuaciones
Data processing
System analysis
Equations
Data-Driven Control
Koopman Operator
Optimization
Control con datos
Operador de koopman
optimización
title_short Operator theory in dynamical network systems
title_full Operator theory in dynamical network systems
title_fullStr Operator theory in dynamical network systems
title_full_unstemmed Operator theory in dynamical network systems
title_sort Operator theory in dynamical network systems
dc.creator.fl_str_mv Téllez Castro, Duván Andrés
dc.contributor.advisor.none.fl_str_mv Mojica Nava, Eduardo Alirio
Sofrony, Jorge
dc.contributor.author.none.fl_str_mv Téllez Castro, Duván Andrés
dc.contributor.researchgroup.spa.fl_str_mv Programa de Investigacion sobre Adquisicion y Analisis de Señales Paas-Un
dc.subject.ddc.spa.fl_str_mv 000 - Ciencias de la computación, información y obras generales
620 - Ingeniería y operaciones afines
topic 000 - Ciencias de la computación, información y obras generales
620 - Ingeniería y operaciones afines
Procesamiento de datos
Análisis de sistemas
Ecuaciones
Data processing
System analysis
Equations
Data-Driven Control
Koopman Operator
Optimization
Control con datos
Operador de koopman
optimización
dc.subject.lemb.spa.fl_str_mv Procesamiento de datos
Análisis de sistemas
Ecuaciones
dc.subject.lemb.eng.fl_str_mv Data processing
System analysis
Equations
dc.subject.proposal.eng.fl_str_mv Data-Driven Control
Koopman Operator
Optimization
dc.subject.proposal.spa.fl_str_mv Control con datos
Operador de koopman
optimización
description gráficas, ilustraciones, tablas
publishDate 2022
dc.date.accessioned.none.fl_str_mv 2022-08-26T20:46:28Z
dc.date.available.none.fl_str_mv 2022-08-26T20:46:28Z
dc.date.issued.none.fl_str_mv 2022-08-25
dc.type.spa.fl_str_mv Trabajo de grado - Doctorado
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/doctoralThesis
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_db06
dc.type.content.spa.fl_str_mv Text
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/TD
format http://purl.org/coar/resource_type/c_db06
status_str acceptedVersion
dc.identifier.uri.none.fl_str_mv https://repositorio.unal.edu.co/handle/unal/82146
dc.identifier.instname.spa.fl_str_mv Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv https://repositorio.unal.edu.co/
url https://repositorio.unal.edu.co/handle/unal/82146
https://repositorio.unal.edu.co/
identifier_str_mv Universidad Nacional de Colombia
Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv [1] Alvarado, Ignacio ; Limon, Daniel ; De La Pen ̃a, D M. ; Maestre, Jos ́e M. ; Ridao, MA ; Scheu, H ; Marquardt, W ; Negenborn, RR ; De Schutter, B ; Valencia, F ; Espinosa, J: A comparative analysis of distributed MPC techniques applied to the HD-MPC four-tank benchmark. En: Journal of Process Control 21 (2011), Nr. 5, p. 800–815
[2] Arbabi, Hassan ; Mezic, Igor: Ergodic theory, dynamic mode decomposition, and computation of spectral properties of the Koopman operator. En: SIAM Journal on Applied Dynamical Systems 16 (2017), Nr. 4, p. 2096–2126
[3] Arrow, Kenneth J. ; Azawa, Hirofumi ; Hurwicz, Leonid ; Uzawa, Hirofumi: Studies in linear and non-linear programming. Vol. 2. First Edition. Stanford University Press, 1958
[4] Asaro, RJ ; Tiller, WA: Interface morphology development during stress corrosion cracking: Part I. Via surface diffusion. En: Metallurgical and Materials Transactions B 3 (1972), Nr. 7, p. 1789–1796
[5] Bach, Francis ; Jordan, Michael: Learning spectral clustering. En: Advances in neural information processing systems 16 (2004), Nr. 2, p. 305–312
[6] Baggio, Giacomo ; Bassett, Danielle S. ; Pasqualetti, Fabio: Data-driven control of complex networks. En: Nature communications 12 (2021), Nr. 1, p. 1–13
[7] Bakker, Craig ; Rosenthal, Steven ; Nowak, Kathleen E.: Koopman Representa- tions of Dynamic Systems with Control. En: arXiv preprint arXiv:1908.02233 (2019)
[8] Baldi, Simone ; Frasca, Paolo: Adaptive synchronization of unknown heterogeneous agents: An adaptive virtual model reference approach. En: Journal of the Franklin Institute (2018). – ISSN 0016–0032
[9] Bertsekas, Dimitri ; Nedic, Angelia ; Ozdaglar, Asuman: Convex Analysis and Optimization. First Edition. Athena scientific Belmont, 2003. – ISBN 2002092168
[10] Bevanda, Petar ; Sosnowski, Stefan ; Hirche, Sandra. Koopman Operator Dynami- cal Models: Learning, Analysis and Control. 2021
[11] Bittracher, Andreas ; Koltai, P ́eter ; Klus, Stefan ; Banisch, Ralf ; Dellnitz, Michael ; Schu ̈tte, Christof: Transition manifolds of complex metastable systems. En: Journal of nonlinear science 28 (2018), Nr. 2, p. 471–512
[12] Bollt, Erik M. ; Santitissadeekorn, Naratip: Applied and computational measura- ble dynamics. First Edition. SIAM, 2013
[13] Brunton, Steven L. ; Brunton, Bingni W. ; Proctor, Joshua L. ; Kutz, J N.: Koopman invariant subspaces and finite linear representations of nonlinear dynamical systems for control. En: PloS one 11 (2016), Nr. 2, p. e0150171
[14] Brunton, Steven L. ; Brunton, Bingni W. ; Proctor, Joshua L. ; Kutz, J. N.: Koopman invariant subspaces and finite linear representations of nonlinear dynamical systems for control. En: PLoS ONE 11 (2016), Nr. 2. – ISSN 19326203
[15] Brunton, Steven L. ; Kutz, J N.: Methods for data-driven multiscale model discovery for materials. En: Journal of Physics: Materials 2 (2019), Nr. 4, p. 044002
[16] Budiˇsic ́, Marko ; Mohr, Ryan ; Mezic ́, Igor: Applied Koopmanism. En: Chaos: An Interdisciplinary Journal of Nonlinear Science 22 (2012), Nr. 4, p. 047510
[17] Budiˇsic ́, Marko ; Mohr, Ryan ; Mezic ́, Igor: Applied koopmanism. En: Chaos: An Interdisciplinary Journal of Nonlinear Science 22 (2012), Nr. 4, p. 047510
[18] Budi ̊A¡iA ̈‡, Marko ; MeziA ̈‡, Igor: An approximate parametrization of the ergodic partition using time averaged observables. En: Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference, 2009, p. 3162–3168
[19] Burbano Lombana, Daniel A. ; Di Bernardo, Mario: Synchronization and lo- cal convergence analysis of networks with dynamic diffusive coupling. En: Chaos: An Interdisciplinary Journal of Nonlinear Science 26 (2016), Nr. 11, p. 116308
[20] Cahn, John W. ; Hilliard, John E.: Free energy of a nonuniform system. I. Interfacial free energy. En: The Journal of chemical physics 28 (1958), Nr. 2, p. 258–267
[21] Cai, He ; Lewis, Frank L. ; Hu, Guoqiang ; Huang, Jie: The adaptive distributed observer approach to the cooperative output regulation of linear multi-agent systems. En: Automatica (2017)
[22] Cherukuri, Ashish ; Gharesifard, Bahman ; Cortes, Jorge: Saddle-point dyna- mics: conditions for asymptotic stability of saddle points. En: SIAM Journal on Control and Optimization 55 (2017), Nr. 1, p. 486–511
[23] Cherukuri, Ashish ; Mallada, Enrique ; Low, Steven ; Cortes, Jorge: The Role of Convexity in Saddle-Point Dynamics: Lyapunov Function and Robustness. En: IEEE Transactions on Automatic Control 63 (2018), Nr. 8, p. 2449–2464
[24] Dietrich, Felix ; Thiem, Thomas N. ; Kevrekidis, Ioannis G.: On the Koopman operator of algorithms. En: SIAM Journal on Applied Dynamical Systems 19 (2020), Nr. 2, p. 860–885
[25] Dogra, Akshunna S. ; Redman, William: Optimizing Neural Networks via Koopman Operator Theory. En: Larochelle, H. (Ed.) ; Ranzato, M. (Ed.) ; Hadsell, R. (Ed.) ; Balcan, M. F. (Ed.) ; Lin, H. (Ed.): Advances in Neural Information Processing Systems Vol. 33, Curran Associates, Inc., 2020, p. 2087–2097
[26] Dutra, Max S. ; de Pina Filho, Armando C. ; Romano, Vitor F.: Modeling of a bipedal locomotor using coupled nonlinear oscillators of Van der Pol. En: Biological Cybernetics 88 (2003), Nr. 4, p. 286–292
[27] Ferreau, Hans J. ; Kirches, Christian ; Potschka, Andreas ; Bock, Hans G. ; Diehl, Moritz: qpOASES: A parametric active-set algorithm for quadratic program- ming. En: Mathematical Programming Computation 6 (2014), Nr. 4, p. 327–363
[28] Garcia-Tenorio, Camilo ; Delansnay, Gilles ; Mojica-Nava, Eduardo ; Van- de Wouwer, Alain: Trigonometric Embeddings in Polynomial Extended Mode De- composition -Experimental Application to an Inverted Pendulum. En: Mathematics 9 (2021), Nr. 10, p. 1119
[29] Giraldo, Jairo ; Mojica-Nava, Eduardo ; Quijano, Nicanor: Synchronization of isolated microgrids with a communication infrastructure using energy storage systems. En: International Journal of Electrical Power & Energy Systems 63 (2014), p. 71–82
[30] Gladkikh, AA ; Malinetskii, GG: Study of dynamical systems from the viewpoint of complexity and computational capabilities. En: Differential Equations 52 (2016), Nr. 7, p. 897–905
[31] Gutie ́rrez, Manuel S. ; Lucarini, Valerio ; Chekroun, Micka ̈el D ; Ghil, Michael: Reduced-Order Models for Coupled Dynamical Systems: Koopman Operator and Data- driven Methods. En: arXiv preprint arXiv:2012.01068 (2020)
[32] Heersink, Byron ; Warren, Michael A. ; Hoffmann, Heiko: Dynamic mode de- composition for interconnected control systems. En: arXiv preprint arXiv:1709.02883 (2017)
[33] Helmke, Uwe ; Moore, John B.: Optimization and dynamical systems. First Edition. Springer Science & Business Media, 2012
[34] Hohenberg, P. C. ; Halperin, B. I.: Theory of dynamic critical phenomena. En: Rev. Mod. Phys. 49 (1977), Jul, p. 435–479
[35] Hu, Jiangping ; Hong, Yiguang: Leader-following coordination of multi-agent systems with coupling time delays. En: Physica A: Statistical Mechanics and its Applications (2007)
[36] Huang, Bowen ; Ma, Xu ; Vaidya, Umesh: Data-Driven Nonlinear Stabilization Using Koopman Operator. En: arXiv preprint arXiv:1901.07678 (2019)
[37] Huang, Jie: Nonlinear output regulation: theory and applications. 1. Society for Industrial and Applied Mathematics, 2004 (Advances in design and control). – ISBN 9780898715620,0898715628
[38] Isidori, Alberto ; Byrnes, Christopher I.: Output regulation of nonlinear systems. En: IEEE transactions on Automatic Control 35 (1990), Nr. 2, p. 131–140
[39] Jain, Sandeep ; Khorrami, Farshad: Decentralized adaptive control of a class of large- scale interconnected nonlinear systems. En: IEEE Transactions on Automatic Control 42 (1997), Nr. 2, p. 136–154
[40] Jiang, Yu ; Jiang, Zhong-Ping: Computational adaptive optimal control for continuous-time linear systems with completely unknown dynamics. En: Automatica 48 (2012), Nr. 10, p. 2699–2704
[41] Khalil, Hassan K.: Nonlinear systems. Vol. 3. First Edition. Prentice hall Upper Saddle River, NJ, 2002
[42] Klus, Stefan ; Koltai, P ́eter ; Schu ̈tte, Christof: On the numerical approximation of the Perron-Frobenius and Koopman operator. En: arXiv preprint arXiv:1512.05997 (2015)
[43] Klus, Stefan ; Nu ̈ske, Feliks ; Peitz, Sebastian ; Niemann, Jan-Hendrik ; Clementi, Cecilia ; Schu ̈tte, Christof: Data-driven approximation of the Koopman generator: Model reduction, system identification, and control. En: Physica D: Nonlinear Pheno- mena 406 (2020), p. 132416
[44] Klus, Stefan ; Schuster, Ingmar ; Muandet, Krikamol: Eigendecompositions of transfer operators in reproducing kernel Hilbert spaces. En: Journal of Nonlinear Scien- ce 30 (2020), Nr. 1, p. 283–315
[45] Koopman, Bernard O.: Hamiltonian systems and transformation in Hilbert space. En: Proceedings of the national academy of sciences of the united states of america 17 (1931), Nr. 5, p. 315
[46] Korda, Milan ; Mezic ́, Igor: Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control. En: Automatica 93 (2018), p. 149–160
[47] Korda, Milan ; Mezic ́, Igor: Linear predictors for nonlinear dynamical systems: Koop- man operator meets model predictive control. En: Automatica 93 (2018), p. 149–160
[48] Korda, Milan ; MeziA ̈‡, Igor: Optimal Construction of Koopman Eigenfunctions for Prediction and Control. En: IEEE Transactions on Automatic Control 65 (2020), Nr. 12, p. 5114–5129
[49] Kose, T: Solutions of saddle value problems by differential equations. En: Econome- trica, Journal of the Econometric Society (1956), p. 59–70
[50] Kutz, J N. ; Brunton, Steven L. ; Brunton, Bingni W. ; Proctor, Joshua L.: Dynamic mode decomposition: data-driven modeling of complex systems. First Edition. SIAM, 2016
[51] Langer, James S.: Instabilities and pattern formation in crystal growth. En: Reviews of modern physics 52 (1980), Nr. 1, p. 1
[52] Lasota, Andrzej ; Mackey, Michael C.: Chaos, fractals, and noise: stochastic aspects of dynamics. Vol. 97. First Edition. Springer Science & Business Media, 2013
[53] Levine, Sergey ; Pastor, Peter ; Krizhevsky, Alex ; Ibarz, Julian ; Quillen, Deirdre: Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection. En: The International Journal of Robotics Research 37 (2018), Nr. 4-5, p. 421–436
[54] Li, Shaoyuan ; Zheng, Yi: Distributed model predictive control for plant-wide systems. 1. John Wiley & Sons, 2016
[55] Liu, Zhiyuan ; Kundu, Soumya ; Chen, Lijun ; Yeung, Enoch: Decomposition of nonlinear dynamical systems using koopman gramians. En: 2018 Annual American Control Conference (ACC) IEEE, 2018, p. 4811–4818
[56] Lymperopoulos, Georgios ; Ioannou, Petros: Model reference adaptive control for networked distributed systems with strong interconnections and communication delays. En: Journal of Systems Science and Complexity 31 (2018), Nr. 1, p. 38–68
[57] Mauroy, Alexandre ; Goncalves, Jorge: Koopman-based lifting techniques for non- linear systems identification. En: IEEE Transactions on Automatic Control (2019)
[58] Mauroy, Alexandre ; Goncalves, Jorge: Koopman-based lifting techniques for non- linear systems identification. En: IEEE Transactions on Automatic Control 65 (2019), Nr. 6, p. 2550–2565
[59] Mauroy, Alexandre ; Hendrickx, Julien: Spectral identification of networks using sparse measurements. En: SIAM Journal on Applied Dynamical Systems 16 (2017), Nr. 1, p. 479–513
[60] Mauroy, Alexandre ; Mezic ́, Igor: Global stability analysis using the eigenfunctions of the Koopman operator. En: IEEE Transactions on Automatic Control 61 (2016), Nr. 11, p. 3356–3369
[61] Mauroy, Alexandre ; Mezic ́, Igor ; Susuki, Yoshihiko: The Koopman Operator in Sys- tems and Control: Concepts, Methodologies, and Applications. Vol. 484. First Edition. Springer Nature, 2020
[62] Mezic ́, Igor: Spectral properties of dynamical systems, model reduction and decompo- sitions. En: Nonlinear Dynamics 41 (2005), Nr. 1, p. 309–325
[63] Mezic ́, Igor: Koopman Operator, Geometry, and Learning of Dynamical Systems. En: Notices of the American Mathematical Society 68 (2021), Nr. 7, p. 1087–1105
[64] Modares, H. ; Lewis, F. L.: Linear Quadratic Tracking Control of Partially-Unknown Continuous-Time Systems Using Reinforcement Learning. En: IEEE Transactions on Automatic Control 59 (2014), Nr. 11, p. 3051–3056. – ISSN 0018–9286
[65] Modares, Hamidreza ; Lewis, Frank L. ; Kang, Wei ; Davoudi, Ali: Optimal Syn- chronization of Heterogeneous Nonlinear Systems With Unknown Dynamics. En: IEEE Transactions on Automatic Control 63 (2018), Nr. 1, p. 117–131
[66] Modares, Hamidreza ; Nageshrao, Subramanya P. ; Lopes, Gabriel A D. ; Ba- buˇska, Robert ; Lewis, Frank L.: Optimal model-free output synchronization of hete- rogeneous systems using off-policy reinforcement learning. En: Automatica 71 (2016), p. 334–341
[67] Molnar, Ferenc ; Nishikawa, Takashi ; Motter, Adilson E.: Asymmetry underlies stability in power grids. En: Nature communications 12 (2021), Nr. 1, p. 1–9
[68] Mullins, W. W.: Two-Dimensional Motion of Idealized Grain Boundaries. En: J. App. Phys. 27 (1956), Nr. 8, p. 900–904
[69] Nandanoori, Sai P. ; Sinha, Subhrajit ; Yeung, Enoch: Data-driven operator theo- retic methods for global phase space learning. En: 2020 American Control Conference (ACC) IEEE, 2020, p. 4551–4557
[70] Ng, Andrew Y. ; Jordan, Michael I. ; Weiss, Yair: On spectral clustering: Analysis and an algorithm. En: Advances in neural information processing systems, 2002, p. 849–856
[71] Niemann, Jan-Hendrik ; Klus, Stefan ; Schu ̈tte, Christof: Data-driven model re- duction of agent-based systems using the Koopman generator. En: PloS one 16 (2021), Nr. 5, p. e0250970
[72] Novick-Cohen, Amy ; Segel, Lee A.: Nonlinear aspects of the Cahn-Hilliard equa- tion. En: Physica D: Nonlinear Phenomena 10 (1984), Nr. 3, p. 277–298
[73] Otto, Samuel E. ; Rowley, Clarence W.: Koopman operators for estimation and control of dynamical systems. En: Annual Review of Control, Robotics, and Autonomous Systems 4 (2021), p. 59–87
[74] Proctor, Joshua L. ; Brunton, Steven L. ; Kutz, J N.: Generalizing Koopman theory to allow for inputs and control. En: SIAM Journal on Applied Dynamical Systems 17 (2018), Nr. 1, p. 909–930
[75] Rabben, Robert J. ; Ray, Sourav ; Weber, Marcus: ISOKANN: Invariant subspaces of Koopman operators learned by a neural network. En: The Journal of Chemical Physics 153 (2020), Nr. 11, p. 114109
[76] Raghunathan, Arvind ; Vaidya, Umesh: Optimal stabilization using lyapunov mea- sures. En: IEEE Transactions on Automatic Control 59 (2013), Nr. 5, p. 1316–1321
[77] Santos Gutie ́rrez, Manuel ; Lucarini, Valerio ; Chekroun, Micka ̈el D ; Ghil, Michael: Reduced-order models for coupled dynamical systems: Data-driven methods and the Koopman operator. En: Chaos: An Interdisciplinary Journal of Nonlinear Science 31 (2021), Nr. 5, p. 053116
[78] Sarsilmaz, S. B. ; Yucelen, T.: On Control of Heterogeneous Multiagent Systems: A Dynamic Measurement Output Feedback Approach. En: 2018 Annual American Control Conference (ACC), 2018. – ISSN 2378–5861, p. 1268–1273
[79] Sarsilmaz, S B. ; Yucelen, Tansel: On control of heterogeneous multiagent sys- tems with unknown leader dynamics. En: ASME 2017 Dynamic Systems and Control Conference, 2017
[80] Sinha, Subhrajit ; Huang, Bowen ; Vaidya, Umesh: Robust approximation of koop- man operator and prediction in random dynamical systems. En: 2018 Annual American Control Conference (ACC) IEEE, 2018, p. 5491–5496
[81] Sinha, Subhrajit ; Nandanoori, Sai P. ; Yeung, Enoch: Computationally Efficient Learning of Large Scale Dynamical Systems: A Koopman Theoretic Approach. En: arXiv preprint arXiv:2007.00835 (2020)
[82] Spooner, Jeffrey T. ; Passino, Kevin M.: Decentralized adaptive control of nonli- near systems using radial basis neural networks. En: IEEE Transactions on Automatic Control 44 (1999), Nr. 11, p. 2050–2057
[83] Su, Youfeng ; Huang, Jie: Cooperative adaptive output regulation for a class of non- linear uncertain multi-agent systems with unknown leader. En: Systems & Control Letters 62 (2013), Nr. 6, p. 461 – 467. – ISSN 0167–6911
[84] Von Luxburg, Ulrike: A tutorial on spectral clustering. En: Statistics and computing 17 (2007), Nr. 4, p. 395–416
[85] Williams, Matthew O. ; Kevrekidis, Ioannis G. ; Rowley, Clarence W.: A Data- Driven Approximation of the Koopman Operator: Extending Dynamic Mode Decom- position. En: Journal of Nonlinear Science 25 (2015), Nr. 6, p. 1307–1346. – ISSN 14321467
[86] Williams, Matthew O. ; Kevrekidis, Ioannis G. ; Rowley, Clarence W.: A data– driven approximation of the koopman operator: Extending dynamic mode decomposi- tion. En: Journal of Nonlinear Science 25 (2015), Nr. 6, p. 1307–1346
[87] Xie, Tian ; France-Lanord, Arthur ; Wang, Yanming ; Shao-Horn, Yang ; Gross- man, Jeffrey C.: Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials. En: Nature communications 10 (2019), Nr. 1, p. 1–9
[88] Yi, Bowen ; Manchester, Ian R.: On the equivalence of contraction and Koopman approaches for nonlinear stability and control. En: arXiv preprint arXiv:2103.15033 (2021)
[89] Yosida, Kˆosaku: Functional analysis. First Edition. Springer Berlin Heidelberg, 1971
[90] Zuo, Shan ; Song, Yongduan ; Lewis, Frank L. ; Davoudi, Ali: Adaptive output containment control of heterogeneous multi-agent systems with unknown leaders. En: Automatica 92 (2018), p. 235 – 239. – ISSN 0005–1098
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.license.spa.fl_str_mv Atribución-SinDerivadas 4.0 Internacional
dc.rights.uri.spa.fl_str_mv http://creativecommons.org/licenses/by-nd/4.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución-SinDerivadas 4.0 Internacional
http://creativecommons.org/licenses/by-nd/4.0/
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.extent.spa.fl_str_mv xi, 99 páginas
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv Bogotá - Ingeniería - Doctorado en Ingeniería - Ingeniería Eléctrica
dc.publisher.department.spa.fl_str_mv Departamento de Ingeniería Eléctrica y Electrónica
dc.publisher.faculty.spa.fl_str_mv Facultad de Ingeniería
dc.publisher.place.spa.fl_str_mv Bogotá, Colombia
dc.publisher.branch.spa.fl_str_mv Universidad Nacional de Colombia - Sede Bogotá
institution Universidad Nacional de Colombia
bitstream.url.fl_str_mv https://repositorio.unal.edu.co/bitstream/unal/82146/1/license.txt
https://repositorio.unal.edu.co/bitstream/unal/82146/3/1018427055.2022.pdf
https://repositorio.unal.edu.co/bitstream/unal/82146/4/1018427055.2022.pdf.jpg
bitstream.checksum.fl_str_mv 8a4605be74aa9ea9d79846c1fba20a33
6d158038eac203276d9483efa4a455d6
178347c57597d977272ba9cc56c390c1
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
repository.name.fl_str_mv Repositorio Institucional Universidad Nacional de Colombia
repository.mail.fl_str_mv repositorio_nal@unal.edu.co
_version_ 1806886221075447808
spelling Atribución-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Mojica Nava, Eduardo Alirio609c35fb4a7e288ee81a2ef0fb802397Sofrony, Jorgee0a5bffc1b8e865df36e2a58fd8a342eTéllez Castro, Duván Andrés41119cb00ab4b3c16ad1afe543e48611Programa de Investigacion sobre Adquisicion y Analisis de Señales Paas-Un2022-08-26T20:46:28Z2022-08-26T20:46:28Z2022-08-25https://repositorio.unal.edu.co/handle/unal/82146Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/gráficas, ilustraciones, tablasWe provide a data-driven synthesis framework for some complex systems. The proposed fra- mework relies on the linear operator theory involving the Koopman operator. Our first results employ Koopman-based lifting for the identification of linear models from the data both un- der the controlled and uncontrolled settings. Spectral analysis of Koopman and its adjoint Perron-Frobenius operator helps us identify the invariant structure and dominant modes for the reduced-order representation from the data. Our second result is a design methodology of a model-free and decentralized control strategy for interconnected systems. We provi- de a predictive control for decoupling the systems using the linear operator. Additionally, we address a distributed output regulation algorithm for the leader-follower heterogeneous multi-agent system with unknown leader dynamics. The leader modeling is learned through the Koopman operator and the regulator is developed using optimal control theory. Finally, we develop a technique using the Koopman operator to obtain a data-driven continuous-time optimization algorithm for solving constrained optimization problems using its connection with dynamical systems for numerical algorithms. (Text taken from source)En esta tesis proporcionamos un marco de síntesis basado en datos para algunos sistemas complejos. El marco propuesto se basa en la teoría del operador lineal que involucra al operador de Koopman. Nuestros primeros resultados emplean el espacio Koopman-lifted para la identificación de modelos lineales a partir de los datos, tanto en entornos controlados como no controlados. El análisis espectral de Koopman y su operador adjunto Perron-Frobenius nos ayuda a identificar la estructura invariante y los modos dominantes para la representación de orden reducido a partir de los datos. Nuestro segundo resultado es una metodología de diseño de una estrategia de control descentralizada y sin modelo para sistemas interconectados. Proporcionamos un control predictivo para el desacoplamiento de los sistemas mediante el operador lineal. Además, abordamos un algoritmo de regulación de salida distribuida para el sistema heterogéneo de múltiples agentes tipo líder-seguidor con una dinámica de líder desconocida. El modelo de líder se aprende a través del operador de Koopman y el regulador se desarrolla utilizando la teoría de control óptimo. Finalmente, desarrollamos una técnica utilizando el operador de Koopman para obtener un algoritmo de optimización de tiempo continuo basado en datos para resolver problemas de optimización restringida usando su conexión con sistemas dinámicos para algoritmos numéricos.DoctoradoDoctor en IngenieríaControl Distribuidoxi, 99 páginasapplication/pdfengUniversidad Nacional de ColombiaBogotá - Ingeniería - Doctorado en Ingeniería - Ingeniería EléctricaDepartamento de Ingeniería Eléctrica y ElectrónicaFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá000 - Ciencias de la computación, información y obras generales620 - Ingeniería y operaciones afinesProcesamiento de datosAnálisis de sistemasEcuacionesData processingSystem analysisEquationsData-Driven ControlKoopman OperatorOptimizationControl con datosOperador de koopmanoptimizaciónOperator theory in dynamical network systemsTeoría de operadores en sistemas dinámicos en redTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06Texthttp://purl.org/redcol/resource_type/TD[1] Alvarado, Ignacio ; Limon, Daniel ; De La Pen ̃a, D M. ; Maestre, Jos ́e M. ; Ridao, MA ; Scheu, H ; Marquardt, W ; Negenborn, RR ; De Schutter, B ; Valencia, F ; Espinosa, J: A comparative analysis of distributed MPC techniques applied to the HD-MPC four-tank benchmark. En: Journal of Process Control 21 (2011), Nr. 5, p. 800–815[2] Arbabi, Hassan ; Mezic, Igor: Ergodic theory, dynamic mode decomposition, and computation of spectral properties of the Koopman operator. En: SIAM Journal on Applied Dynamical Systems 16 (2017), Nr. 4, p. 2096–2126[3] Arrow, Kenneth J. ; Azawa, Hirofumi ; Hurwicz, Leonid ; Uzawa, Hirofumi: Studies in linear and non-linear programming. Vol. 2. First Edition. Stanford University Press, 1958[4] Asaro, RJ ; Tiller, WA: Interface morphology development during stress corrosion cracking: Part I. Via surface diffusion. En: Metallurgical and Materials Transactions B 3 (1972), Nr. 7, p. 1789–1796[5] Bach, Francis ; Jordan, Michael: Learning spectral clustering. En: Advances in neural information processing systems 16 (2004), Nr. 2, p. 305–312[6] Baggio, Giacomo ; Bassett, Danielle S. ; Pasqualetti, Fabio: Data-driven control of complex networks. En: Nature communications 12 (2021), Nr. 1, p. 1–13[7] Bakker, Craig ; Rosenthal, Steven ; Nowak, Kathleen E.: Koopman Representa- tions of Dynamic Systems with Control. En: arXiv preprint arXiv:1908.02233 (2019)[8] Baldi, Simone ; Frasca, Paolo: Adaptive synchronization of unknown heterogeneous agents: An adaptive virtual model reference approach. En: Journal of the Franklin Institute (2018). – ISSN 0016–0032[9] Bertsekas, Dimitri ; Nedic, Angelia ; Ozdaglar, Asuman: Convex Analysis and Optimization. First Edition. Athena scientific Belmont, 2003. – ISBN 2002092168[10] Bevanda, Petar ; Sosnowski, Stefan ; Hirche, Sandra. Koopman Operator Dynami- cal Models: Learning, Analysis and Control. 2021[11] Bittracher, Andreas ; Koltai, P ́eter ; Klus, Stefan ; Banisch, Ralf ; Dellnitz, Michael ; Schu ̈tte, Christof: Transition manifolds of complex metastable systems. En: Journal of nonlinear science 28 (2018), Nr. 2, p. 471–512[12] Bollt, Erik M. ; Santitissadeekorn, Naratip: Applied and computational measura- ble dynamics. First Edition. SIAM, 2013[13] Brunton, Steven L. ; Brunton, Bingni W. ; Proctor, Joshua L. ; Kutz, J N.: Koopman invariant subspaces and finite linear representations of nonlinear dynamical systems for control. En: PloS one 11 (2016), Nr. 2, p. e0150171[14] Brunton, Steven L. ; Brunton, Bingni W. ; Proctor, Joshua L. ; Kutz, J. N.: Koopman invariant subspaces and finite linear representations of nonlinear dynamical systems for control. En: PLoS ONE 11 (2016), Nr. 2. – ISSN 19326203[15] Brunton, Steven L. ; Kutz, J N.: Methods for data-driven multiscale model discovery for materials. En: Journal of Physics: Materials 2 (2019), Nr. 4, p. 044002[16] Budiˇsic ́, Marko ; Mohr, Ryan ; Mezic ́, Igor: Applied Koopmanism. En: Chaos: An Interdisciplinary Journal of Nonlinear Science 22 (2012), Nr. 4, p. 047510[17] Budiˇsic ́, Marko ; Mohr, Ryan ; Mezic ́, Igor: Applied koopmanism. En: Chaos: An Interdisciplinary Journal of Nonlinear Science 22 (2012), Nr. 4, p. 047510[18] Budi ̊A¡iA ̈‡, Marko ; MeziA ̈‡, Igor: An approximate parametrization of the ergodic partition using time averaged observables. En: Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference, 2009, p. 3162–3168[19] Burbano Lombana, Daniel A. ; Di Bernardo, Mario: Synchronization and lo- cal convergence analysis of networks with dynamic diffusive coupling. En: Chaos: An Interdisciplinary Journal of Nonlinear Science 26 (2016), Nr. 11, p. 116308[20] Cahn, John W. ; Hilliard, John E.: Free energy of a nonuniform system. I. Interfacial free energy. En: The Journal of chemical physics 28 (1958), Nr. 2, p. 258–267[21] Cai, He ; Lewis, Frank L. ; Hu, Guoqiang ; Huang, Jie: The adaptive distributed observer approach to the cooperative output regulation of linear multi-agent systems. En: Automatica (2017)[22] Cherukuri, Ashish ; Gharesifard, Bahman ; Cortes, Jorge: Saddle-point dyna- mics: conditions for asymptotic stability of saddle points. En: SIAM Journal on Control and Optimization 55 (2017), Nr. 1, p. 486–511[23] Cherukuri, Ashish ; Mallada, Enrique ; Low, Steven ; Cortes, Jorge: The Role of Convexity in Saddle-Point Dynamics: Lyapunov Function and Robustness. En: IEEE Transactions on Automatic Control 63 (2018), Nr. 8, p. 2449–2464[24] Dietrich, Felix ; Thiem, Thomas N. ; Kevrekidis, Ioannis G.: On the Koopman operator of algorithms. En: SIAM Journal on Applied Dynamical Systems 19 (2020), Nr. 2, p. 860–885[25] Dogra, Akshunna S. ; Redman, William: Optimizing Neural Networks via Koopman Operator Theory. En: Larochelle, H. (Ed.) ; Ranzato, M. (Ed.) ; Hadsell, R. (Ed.) ; Balcan, M. F. (Ed.) ; Lin, H. (Ed.): Advances in Neural Information Processing Systems Vol. 33, Curran Associates, Inc., 2020, p. 2087–2097[26] Dutra, Max S. ; de Pina Filho, Armando C. ; Romano, Vitor F.: Modeling of a bipedal locomotor using coupled nonlinear oscillators of Van der Pol. En: Biological Cybernetics 88 (2003), Nr. 4, p. 286–292[27] Ferreau, Hans J. ; Kirches, Christian ; Potschka, Andreas ; Bock, Hans G. ; Diehl, Moritz: qpOASES: A parametric active-set algorithm for quadratic program- ming. En: Mathematical Programming Computation 6 (2014), Nr. 4, p. 327–363[28] Garcia-Tenorio, Camilo ; Delansnay, Gilles ; Mojica-Nava, Eduardo ; Van- de Wouwer, Alain: Trigonometric Embeddings in Polynomial Extended Mode De- composition -Experimental Application to an Inverted Pendulum. En: Mathematics 9 (2021), Nr. 10, p. 1119[29] Giraldo, Jairo ; Mojica-Nava, Eduardo ; Quijano, Nicanor: Synchronization of isolated microgrids with a communication infrastructure using energy storage systems. En: International Journal of Electrical Power & Energy Systems 63 (2014), p. 71–82[30] Gladkikh, AA ; Malinetskii, GG: Study of dynamical systems from the viewpoint of complexity and computational capabilities. En: Differential Equations 52 (2016), Nr. 7, p. 897–905[31] Gutie ́rrez, Manuel S. ; Lucarini, Valerio ; Chekroun, Micka ̈el D ; Ghil, Michael: Reduced-Order Models for Coupled Dynamical Systems: Koopman Operator and Data- driven Methods. En: arXiv preprint arXiv:2012.01068 (2020)[32] Heersink, Byron ; Warren, Michael A. ; Hoffmann, Heiko: Dynamic mode de- composition for interconnected control systems. En: arXiv preprint arXiv:1709.02883 (2017)[33] Helmke, Uwe ; Moore, John B.: Optimization and dynamical systems. First Edition. Springer Science & Business Media, 2012[34] Hohenberg, P. C. ; Halperin, B. I.: Theory of dynamic critical phenomena. En: Rev. Mod. Phys. 49 (1977), Jul, p. 435–479[35] Hu, Jiangping ; Hong, Yiguang: Leader-following coordination of multi-agent systems with coupling time delays. En: Physica A: Statistical Mechanics and its Applications (2007)[36] Huang, Bowen ; Ma, Xu ; Vaidya, Umesh: Data-Driven Nonlinear Stabilization Using Koopman Operator. En: arXiv preprint arXiv:1901.07678 (2019)[37] Huang, Jie: Nonlinear output regulation: theory and applications. 1. Society for Industrial and Applied Mathematics, 2004 (Advances in design and control). – ISBN 9780898715620,0898715628[38] Isidori, Alberto ; Byrnes, Christopher I.: Output regulation of nonlinear systems. En: IEEE transactions on Automatic Control 35 (1990), Nr. 2, p. 131–140[39] Jain, Sandeep ; Khorrami, Farshad: Decentralized adaptive control of a class of large- scale interconnected nonlinear systems. En: IEEE Transactions on Automatic Control 42 (1997), Nr. 2, p. 136–154[40] Jiang, Yu ; Jiang, Zhong-Ping: Computational adaptive optimal control for continuous-time linear systems with completely unknown dynamics. En: Automatica 48 (2012), Nr. 10, p. 2699–2704[41] Khalil, Hassan K.: Nonlinear systems. Vol. 3. First Edition. Prentice hall Upper Saddle River, NJ, 2002[42] Klus, Stefan ; Koltai, P ́eter ; Schu ̈tte, Christof: On the numerical approximation of the Perron-Frobenius and Koopman operator. En: arXiv preprint arXiv:1512.05997 (2015)[43] Klus, Stefan ; Nu ̈ske, Feliks ; Peitz, Sebastian ; Niemann, Jan-Hendrik ; Clementi, Cecilia ; Schu ̈tte, Christof: Data-driven approximation of the Koopman generator: Model reduction, system identification, and control. En: Physica D: Nonlinear Pheno- mena 406 (2020), p. 132416[44] Klus, Stefan ; Schuster, Ingmar ; Muandet, Krikamol: Eigendecompositions of transfer operators in reproducing kernel Hilbert spaces. En: Journal of Nonlinear Scien- ce 30 (2020), Nr. 1, p. 283–315[45] Koopman, Bernard O.: Hamiltonian systems and transformation in Hilbert space. En: Proceedings of the national academy of sciences of the united states of america 17 (1931), Nr. 5, p. 315[46] Korda, Milan ; Mezic ́, Igor: Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control. En: Automatica 93 (2018), p. 149–160[47] Korda, Milan ; Mezic ́, Igor: Linear predictors for nonlinear dynamical systems: Koop- man operator meets model predictive control. En: Automatica 93 (2018), p. 149–160[48] Korda, Milan ; MeziA ̈‡, Igor: Optimal Construction of Koopman Eigenfunctions for Prediction and Control. En: IEEE Transactions on Automatic Control 65 (2020), Nr. 12, p. 5114–5129[49] Kose, T: Solutions of saddle value problems by differential equations. En: Econome- trica, Journal of the Econometric Society (1956), p. 59–70[50] Kutz, J N. ; Brunton, Steven L. ; Brunton, Bingni W. ; Proctor, Joshua L.: Dynamic mode decomposition: data-driven modeling of complex systems. First Edition. SIAM, 2016[51] Langer, James S.: Instabilities and pattern formation in crystal growth. En: Reviews of modern physics 52 (1980), Nr. 1, p. 1[52] Lasota, Andrzej ; Mackey, Michael C.: Chaos, fractals, and noise: stochastic aspects of dynamics. Vol. 97. First Edition. Springer Science & Business Media, 2013[53] Levine, Sergey ; Pastor, Peter ; Krizhevsky, Alex ; Ibarz, Julian ; Quillen, Deirdre: Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection. En: The International Journal of Robotics Research 37 (2018), Nr. 4-5, p. 421–436[54] Li, Shaoyuan ; Zheng, Yi: Distributed model predictive control for plant-wide systems. 1. John Wiley & Sons, 2016[55] Liu, Zhiyuan ; Kundu, Soumya ; Chen, Lijun ; Yeung, Enoch: Decomposition of nonlinear dynamical systems using koopman gramians. En: 2018 Annual American Control Conference (ACC) IEEE, 2018, p. 4811–4818[56] Lymperopoulos, Georgios ; Ioannou, Petros: Model reference adaptive control for networked distributed systems with strong interconnections and communication delays. En: Journal of Systems Science and Complexity 31 (2018), Nr. 1, p. 38–68[57] Mauroy, Alexandre ; Goncalves, Jorge: Koopman-based lifting techniques for non- linear systems identification. En: IEEE Transactions on Automatic Control (2019)[58] Mauroy, Alexandre ; Goncalves, Jorge: Koopman-based lifting techniques for non- linear systems identification. En: IEEE Transactions on Automatic Control 65 (2019), Nr. 6, p. 2550–2565[59] Mauroy, Alexandre ; Hendrickx, Julien: Spectral identification of networks using sparse measurements. En: SIAM Journal on Applied Dynamical Systems 16 (2017), Nr. 1, p. 479–513[60] Mauroy, Alexandre ; Mezic ́, Igor: Global stability analysis using the eigenfunctions of the Koopman operator. En: IEEE Transactions on Automatic Control 61 (2016), Nr. 11, p. 3356–3369[61] Mauroy, Alexandre ; Mezic ́, Igor ; Susuki, Yoshihiko: The Koopman Operator in Sys- tems and Control: Concepts, Methodologies, and Applications. Vol. 484. First Edition. Springer Nature, 2020[62] Mezic ́, Igor: Spectral properties of dynamical systems, model reduction and decompo- sitions. En: Nonlinear Dynamics 41 (2005), Nr. 1, p. 309–325[63] Mezic ́, Igor: Koopman Operator, Geometry, and Learning of Dynamical Systems. En: Notices of the American Mathematical Society 68 (2021), Nr. 7, p. 1087–1105[64] Modares, H. ; Lewis, F. L.: Linear Quadratic Tracking Control of Partially-Unknown Continuous-Time Systems Using Reinforcement Learning. En: IEEE Transactions on Automatic Control 59 (2014), Nr. 11, p. 3051–3056. – ISSN 0018–9286[65] Modares, Hamidreza ; Lewis, Frank L. ; Kang, Wei ; Davoudi, Ali: Optimal Syn- chronization of Heterogeneous Nonlinear Systems With Unknown Dynamics. En: IEEE Transactions on Automatic Control 63 (2018), Nr. 1, p. 117–131[66] Modares, Hamidreza ; Nageshrao, Subramanya P. ; Lopes, Gabriel A D. ; Ba- buˇska, Robert ; Lewis, Frank L.: Optimal model-free output synchronization of hete- rogeneous systems using off-policy reinforcement learning. En: Automatica 71 (2016), p. 334–341[67] Molnar, Ferenc ; Nishikawa, Takashi ; Motter, Adilson E.: Asymmetry underlies stability in power grids. En: Nature communications 12 (2021), Nr. 1, p. 1–9[68] Mullins, W. W.: Two-Dimensional Motion of Idealized Grain Boundaries. En: J. App. Phys. 27 (1956), Nr. 8, p. 900–904[69] Nandanoori, Sai P. ; Sinha, Subhrajit ; Yeung, Enoch: Data-driven operator theo- retic methods for global phase space learning. En: 2020 American Control Conference (ACC) IEEE, 2020, p. 4551–4557[70] Ng, Andrew Y. ; Jordan, Michael I. ; Weiss, Yair: On spectral clustering: Analysis and an algorithm. En: Advances in neural information processing systems, 2002, p. 849–856[71] Niemann, Jan-Hendrik ; Klus, Stefan ; Schu ̈tte, Christof: Data-driven model re- duction of agent-based systems using the Koopman generator. En: PloS one 16 (2021), Nr. 5, p. e0250970[72] Novick-Cohen, Amy ; Segel, Lee A.: Nonlinear aspects of the Cahn-Hilliard equa- tion. En: Physica D: Nonlinear Phenomena 10 (1984), Nr. 3, p. 277–298[73] Otto, Samuel E. ; Rowley, Clarence W.: Koopman operators for estimation and control of dynamical systems. En: Annual Review of Control, Robotics, and Autonomous Systems 4 (2021), p. 59–87[74] Proctor, Joshua L. ; Brunton, Steven L. ; Kutz, J N.: Generalizing Koopman theory to allow for inputs and control. En: SIAM Journal on Applied Dynamical Systems 17 (2018), Nr. 1, p. 909–930[75] Rabben, Robert J. ; Ray, Sourav ; Weber, Marcus: ISOKANN: Invariant subspaces of Koopman operators learned by a neural network. En: The Journal of Chemical Physics 153 (2020), Nr. 11, p. 114109[76] Raghunathan, Arvind ; Vaidya, Umesh: Optimal stabilization using lyapunov mea- sures. En: IEEE Transactions on Automatic Control 59 (2013), Nr. 5, p. 1316–1321[77] Santos Gutie ́rrez, Manuel ; Lucarini, Valerio ; Chekroun, Micka ̈el D ; Ghil, Michael: Reduced-order models for coupled dynamical systems: Data-driven methods and the Koopman operator. En: Chaos: An Interdisciplinary Journal of Nonlinear Science 31 (2021), Nr. 5, p. 053116[78] Sarsilmaz, S. B. ; Yucelen, T.: On Control of Heterogeneous Multiagent Systems: A Dynamic Measurement Output Feedback Approach. En: 2018 Annual American Control Conference (ACC), 2018. – ISSN 2378–5861, p. 1268–1273[79] Sarsilmaz, S B. ; Yucelen, Tansel: On control of heterogeneous multiagent sys- tems with unknown leader dynamics. En: ASME 2017 Dynamic Systems and Control Conference, 2017[80] Sinha, Subhrajit ; Huang, Bowen ; Vaidya, Umesh: Robust approximation of koop- man operator and prediction in random dynamical systems. En: 2018 Annual American Control Conference (ACC) IEEE, 2018, p. 5491–5496[81] Sinha, Subhrajit ; Nandanoori, Sai P. ; Yeung, Enoch: Computationally Efficient Learning of Large Scale Dynamical Systems: A Koopman Theoretic Approach. En: arXiv preprint arXiv:2007.00835 (2020)[82] Spooner, Jeffrey T. ; Passino, Kevin M.: Decentralized adaptive control of nonli- near systems using radial basis neural networks. En: IEEE Transactions on Automatic Control 44 (1999), Nr. 11, p. 2050–2057[83] Su, Youfeng ; Huang, Jie: Cooperative adaptive output regulation for a class of non- linear uncertain multi-agent systems with unknown leader. En: Systems & Control Letters 62 (2013), Nr. 6, p. 461 – 467. – ISSN 0167–6911[84] Von Luxburg, Ulrike: A tutorial on spectral clustering. En: Statistics and computing 17 (2007), Nr. 4, p. 395–416[85] Williams, Matthew O. ; Kevrekidis, Ioannis G. ; Rowley, Clarence W.: A Data- Driven Approximation of the Koopman Operator: Extending Dynamic Mode Decom- position. En: Journal of Nonlinear Science 25 (2015), Nr. 6, p. 1307–1346. – ISSN 14321467[86] Williams, Matthew O. ; Kevrekidis, Ioannis G. ; Rowley, Clarence W.: A data– driven approximation of the koopman operator: Extending dynamic mode decomposi- tion. En: Journal of Nonlinear Science 25 (2015), Nr. 6, p. 1307–1346[87] Xie, Tian ; France-Lanord, Arthur ; Wang, Yanming ; Shao-Horn, Yang ; Gross- man, Jeffrey C.: Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials. En: Nature communications 10 (2019), Nr. 1, p. 1–9[88] Yi, Bowen ; Manchester, Ian R.: On the equivalence of contraction and Koopman approaches for nonlinear stability and control. En: arXiv preprint arXiv:2103.15033 (2021)[89] Yosida, Kˆosaku: Functional analysis. First Edition. Springer Berlin Heidelberg, 1971[90] Zuo, Shan ; Song, Yongduan ; Lewis, Frank L. ; Davoudi, Ali: Adaptive output containment control of heterogeneous multi-agent systems with unknown leaders. En: Automatica 92 (2018), p. 235 – 239. – ISSN 0005–1098COLCIENCIASLICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.unal.edu.co/bitstream/unal/82146/1/license.txt8a4605be74aa9ea9d79846c1fba20a33MD51ORIGINAL1018427055.2022.pdf1018427055.2022.pdfTesis de Doctorado en Ingeniería Eléctricaapplication/pdf3442914https://repositorio.unal.edu.co/bitstream/unal/82146/3/1018427055.2022.pdf6d158038eac203276d9483efa4a455d6MD53THUMBNAIL1018427055.2022.pdf.jpg1018427055.2022.pdf.jpgGenerated Thumbnailimage/jpeg3875https://repositorio.unal.edu.co/bitstream/unal/82146/4/1018427055.2022.pdf.jpg178347c57597d977272ba9cc56c390c1MD54unal/82146oai:repositorio.unal.edu.co:unal/821462023-08-08 23:03:58.852Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.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