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Abstract

This chapter presents a literature review related to the main topics treated in this doctoral dissertation. First, a review of model predictive control (MPC) is made focusing on non-centralized schemes, i.e., the architectures for decentralized and distributed MPC controllers. Therefore, some relevant works related to both decentralized and distributed MPC controllers are discussed. Afterwards, a literature review for the tuning issue of the parameters of the MPC controller is introduced. Secondly, the partitioning of large-scale systems is revised, being an essential aspect in the design of non-centralized controllers considering dynamical coupling, information requirements, among others. As a third topic, a review of game-theoretical approaches applied to engineering problems is shown, presenting their versatility in the design of optimization-based controllers. Finally, preliminary concepts regarding population games, which are used throughout the thesis, are presented and some of their relevant features are pointed out.

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Notes

  1. 1.

    The concepts of cooperative and non-cooperative DMPC controllers are omitted due to the fact that it can create confusion with respect to the cooperative and non-cooperative games, which are discussed throughout this doctoral dissertation.

  2. 2.

    It is assumed that agents are rational in the sense that they are able to make decisions in order to improve their benefits based on current information, i.e., no agent would make a decision that implies a decrement in its current benefits.

References

  1. Wang Liuping (2009) Model predictive control system design and implementation using MATLAB, 1st edn. Springer Publishing Company, Incorporated, Berlin. ISBN 1848823304, 9781848823303

    Google Scholar 

  2. Maciejowski J (2002) Predictive control: with constraints. Pearson Education, Berlin

    MATH  Google Scholar 

  3. Maestre JM Negenborn, RR editors (2014) Distributed model predictive control made easy. Intelligent systems, control and automation: science and engineering, vol 69. Springer, Berlin

    Google Scholar 

  4. Ocampo-Martinez C (2010) Model predictive control of wastewater systems. Advances in industrial control, 1st edn. Springer, Berlin. ISBN 978-1-84996-352-7

    Google Scholar 

  5. Rawlings JB, Mayne DQ (2009) Model predictive control: theory and design. Nob Hill Publishing, ISBN, p 9780975937709

    Google Scholar 

  6. Christofides PD, Scattolini R, Muñoz de la Peña D, Liu J (2013) Distributed model predictive control: A tutorial review and future research directions. Comput Chem Eng 51:21–41

    Article  Google Scholar 

  7. Olaru S, Grancharova A, Lobo Pereira F (2015) Developments in model-based optimization and control. Springer, Berlin

    Book  Google Scholar 

  8. Camponogara E, Jia D, Krogh B, Talukdar S (2002) Distributed model predictive control. IEEE Control Syst Mag 22(1):44–52

    Article  Google Scholar 

  9. Negenborn RR, Maestre JM (2014) Distributed model predictive control: An overview and roadmap of future research opportunities. IEEE Control Syst Mag 34(4):87–97

    Article  MathSciNet  Google Scholar 

  10. Scattolini R (2009) Architectures for distributed and hierarchical model predictive control - A review. J Process Control 19(5):723–731

    Article  Google Scholar 

  11. Mayne D (2014) Model predictive control: Recent developments and future promise. Automatica 50(2014):2967–2986

    Article  MathSciNet  Google Scholar 

  12. Bemporad A, Barcelli D (2010) Decentralized model predictive control. In: Bemporad A, Heemels M, Johansson M (eds) Networked control systems, vol 406. Lecture notes in control and information sciences. London, Springer, pp 149–178

    Chapter  Google Scholar 

  13. Alessio A, Barcelli D, Bemporad A (2011) Decentralized model predictive control of dynamically coupled linear systems. J Process Control 21:705–714

    Article  Google Scholar 

  14. Riverso S, Farina M, Ferrari-Trecate G (2013) Plug-and-play decentralized model predictive control for linear systems. IEEE Trans Autom Control 58(10):2608–2614

    Article  MathSciNet  Google Scholar 

  15. Magni L, Scattolini R (2006) Stabilizing decentralized model predictive control of nonlinear systems. Automatica 42(2006):1231–1236

    Article  MathSciNet  Google Scholar 

  16. Raimondo DM, Magni L, Scattolini R (2007) Decentralized model predictive control of nonlinear systems: An input-to-state stability approach. Int J Robust Nonlinear Control 17:1651–1667

    Article  Google Scholar 

  17. Elliott MS, Rasmussen BP (2013) Decentralized model predictive control of a multi-evaporator air conditioning system. Control Eng Pract 21(2013):1665–1677

    Article  Google Scholar 

  18. Tavakoli A, Negnevitsky M, Muttaqi KM (2016) A decentralized model predictive control for operation of multiple distributed generators in islanded mode. Trans Ind Appl. https://doi.org/10.1109/tia.2016.2616396

    Article  Google Scholar 

  19. Cui H, Jacobsen EW (2002) Performance limitations on decentralized control. J Process Control 12:485–494

    Article  Google Scholar 

  20. Rawlings JB, Stewart BT (2008) Coordinating multiple optimization-based controllers: New opportunities and challenges. J Process Control 18:839–845

    Article  Google Scholar 

  21. Negenborn RR, De Schutter B, Hellendoorn J (2008) Multi-agent model predictive control for transportation networks: serial versus parallel schemes. Appl Artif Intell 21(3):353–366

    Article  Google Scholar 

  22. Dunbar W, Murray W (2006) Distributed receding horizon control for multi-vehicle formation stabilization. Automatica 42:549–558

    Article  MathSciNet  Google Scholar 

  23. Arnold M, Negenborn RR, Andersson G, De Schutter B (2010) Distributed predictive control for energy hub coordination in coupled electricity and gas networks. In: Negenborn RR, Lukszo Z, Hellendoorn H (eds) Intelligent infrastructures. Intelligent systems, control and automation: science and engineering, vol 42. Springer, Netherlands, pp 235–273

    Google Scholar 

  24. Ferramosca A, Limon D, Alvarado I, Camacho EF (2013) Cooperative distributed MPC for tracking. Automatica 49(2013):906–914

    Article  MathSciNet  Google Scholar 

  25. Richards A, How JP (2007) Robust distributed model predictive control. Int J Control 80(9):1517–1531

    Article  MathSciNet  Google Scholar 

  26. Farina M, Scattolini R (2011) Distributed non-cooperative MPC with neighbour-to-neighbour communication. In: Proceedings of the 18th IFAC world congress, pages 404–409, Milan, Italy,

    Google Scholar 

  27. Keviczky T, Borrelli F, Balas G (2004) A study on decentralized receding horizon control for decoupled systems. In: Proceedings of the American control conference (ACC). Boston, USA, pp 4921–4926

    Google Scholar 

  28. Giselsson P, Doan MD, Keviczky T, De Schutter B, Rantzer A (2013) Accelerated gradient methods and dual decomposition in distributed model predictive control. Automatica 49:829–833

    Article  MathSciNet  Google Scholar 

  29. Garriga JL, Soroush M (2010) Model predictive control tuning methods: a review. Ind Eng Chem Res (I&EC) 49:3505–3515

    Article  Google Scholar 

  30. Di Cairano S, Bemporad A (2010) Model predictive control tuning by controller matching. IEEE Trans Autom Control 55:185–190

    Article  MathSciNet  Google Scholar 

  31. Tran QN, Octaviano R, Özkan L, Backx ACPM (2014). Generalized predictive control tuning by controller matching. In: Proceedings of the American control conference (ACC). Portland, USA, pp 4889–4894

    Google Scholar 

  32. Shah G, Engell S (2011) Tuning MPC for desired closed-loop performance for MIMO systems. In: Proceedings of the American control conference (ACC). San Francisco, USA, pp 4404–4409

    Google Scholar 

  33. Ozkan L, Meijs J, Backx ACPM (2012) A frequency domain approach for MPC tuning. In: Proceedings of the symposium on process systems engineering. Singapore, pp 15–19

    Google Scholar 

  34. Waschl H, Alberer D, del Re L (2012) Automatic tuning methods for MPC environments. In Moreno-Díaz R, Pichler F, Quesada-Arencibia A (eds) Computer aided systems theory - EUROCAST 2011: 13th international conference. Revised selected papers, Part II. Springer, Berlin, pp 41–48. ISBN 978-3-642-27579-1

    Google Scholar 

  35. Al-Ghazzawi A, Ali E, Nouh A, Zafiriou E (2001) On-line tuning strategy for model predictive controllers. J Process Control 11:265–284

    Article  Google Scholar 

  36. Schwartz JD, Rivera DE (2006) Simulation-based optimal tuning of model predictive control policies for supply chain management using simultenuous perturbation stochastic approximation. In: Proceedings of the American control conference (ACC). Minneapolis, Minnesota, USA, pp 14–16

    Google Scholar 

  37. Toro R, Ocampo-Martinez C, Logist F, Van Impe J, Puig V (2011) Tuning of predictive controllers for drinking water networked systems. In: Proceedings of the 18th IFAC world congress. Milan, Italy, pp 14507–14512

    Google Scholar 

  38. Yamashita AS, Zanin AC, Odloak D (2016) Tuning the model predictive control of a crude distillation unit. ISA Trans 60:178–190

    Article  Google Scholar 

  39. Wojsznis W, Gudaz J, Blevins T, Mehta A (2003) Practical approach to tuning MPC. ISA Trans 42:149–162

    Article  Google Scholar 

  40. van der Lee JH, Svrcek WY, Young BR (2008) A tuning algorithm for model predictive controllers based on genetic algorithms and fuzzy decision making. ISA Trans 47:53–59

    Article  Google Scholar 

  41. Grosso JM, Ocampo-Martinez C, Puig V (2013) Learning-based tuning of supervisory model predictive control for drinking water networks. Eng Appl Artif Intell 26:1741–1750

    Article  Google Scholar 

  42. Waschl H, Jogensen JB, Huusom JK, del Re L (2014) A tuning approach for offset-free MPC with conditional reference adaptation. In: Proceedings of the 19th world congress. Cape Town, South Africa, pp 24–29

    Google Scholar 

  43. Vallerio M, Impe JV, Logist F (2014) Tuning of NMPC controllers via multi-objective optimisation. Comput Chem Eng 61:38–50

    Article  Google Scholar 

  44. He N, Shi D, Wang J, Forbes M, Backstrom J, Chen T (2015) User friendly robust MPC tuning of uncertain paper-making processes. In: Proceedings of the 9th IFAC symposium on advanced control of chemical processes (ADCHEM), vol 48, pp 1021–1026

    Google Scholar 

  45. Müller MA, Angeli D, Allgöwer F (2014) On the performance of economic model predictive control with self-tuning terminal cost. J Process Control 24:1179–1186

    Article  Google Scholar 

  46. Sezer ME, \(\check{\text{S}}\)iljak DD, (1986) Nested \(\varepsilon -\)decompositions and clustering of complex systems. Automatica 22(3):321–331

    Google Scholar 

  47. Chandan V, Alleyne A (2013) Optimal partitioning for the decentralized thermal control of buildings. IEEE Trans Control Syst Technol 21(5):1756–1770

    Article  Google Scholar 

  48. Kleinberg MR, Miu K, Segal N, Lehmann H, Figura TR (2014) A partitioning method for distributed capacitor control of electric power distribution systems. IEEE Trans Power Syst 29(2):637–644

    Article  Google Scholar 

  49. Nayeripour M, Fallahzadeh-Abarghouei H, Waffenschmidt E, Hasanvand S (2016) Coordinated online voltage management of distributed generationusing network partitioning. Electr Power Syst Res 141(2016):202–209

    Article  Google Scholar 

  50. Xie L, Cai X, Chen J, Su H (2016) GA based decomposition of large scale distributed model predictive control systems. Control Eng Pract 57(2016):111–125

    Article  Google Scholar 

  51. Ocampo-Martinez C, Bovo S, Puig V (2011) Partitioning approach oriented to the decentralised predictive control of large-scale systems. J Process Control 21(2011):775–786

    Article  Google Scholar 

  52. Angeline Ezhilarasi G, Swarup KS (2012) Network partitioning using harmony search and equivalencing for distributed computing. J Parallel Distrib Comput 72(2012):936–943

    Article  Google Scholar 

  53. Kamelian S, Salahshoor K (2015) A novel graph-based partitioning algorithm for large-scale dynamical systems. Int J Syst Sci 46(2):227–245

    Article  MathSciNet  Google Scholar 

  54. Núñez A, Ocampo-Martinez C, Maestre JM (2015) De Schutter B (2015) Time-varying scheme for noncentralized model predictive control of large-scale systems. Math Prob Eng 560702:1–17

    Article  Google Scholar 

  55. Hidalgo-Gallego S, Núñez-Sánchez R, Coto-Millán P (2016) Game theory and port economics: a survey of recent research. J Econ Surv. https://doi.org/10.1111/joes.12171

    Article  Google Scholar 

  56. Hammerstein P, Leimar O (2015) Evolutionary game theory in biology. Handbook of game theory with economic applications 4:575–617

    Article  Google Scholar 

  57. Nowak MA, May RM (1992) Evolutionary games and spatial chaos. Nature 359(6398):826–829

    Article  Google Scholar 

  58. Jaeger G (2008) Applications of game theory in linguistics. Lang Linguist Compass 2(3):406–421

    Article  Google Scholar 

  59. Charilas DE, Panagopoulos AD (2010) A survey on game theory applications in wireless networks. Comput Netw 54(18):3421–3430

    Article  Google Scholar 

  60. Giovanini L (2011) Game approach to distributed model predictive control. IET Control Theory Appl 5(15):1729–1739

    Article  MathSciNet  Google Scholar 

  61. Marden JR, Peyton Young H, Pao LY (2014) Achieving pareto optimality through distributed learning. SIAM J Control Optim 52(5):2753–2770

    Article  MathSciNet  Google Scholar 

  62. Marden J, Shamma J (2015) Game theory and distributed control. Handbook of game theory with economic applications 4:861–899

    Article  Google Scholar 

  63. Quijano N, Ocampo-Martinez C, Barreiro-Gomez J, Obando G, Pantoja A, Mojica-Nava E (2017) The role of population games and evolutionary dynamics in distributed control systems. IEEE Control Syst 37(1):70–97

    Article  MathSciNet  Google Scholar 

  64. Basar T, Olsder GJ (1999) Dynamic noncooperative game theory, vol 23. SIAM

    Google Scholar 

  65. Menache I, Ozdaglar A (2011) Network games: theory, models, and dynamics. Morgan & Claypool Publishers,

    Google Scholar 

  66. Bacci G, Lasaulce S, Saad W, Sanguinetti L (2016) Game theory for networks: A tutorial on game-theoretic tools for emerging signal processing applications. IEEE Signal Process Mag 33(1):94–119

    Article  Google Scholar 

  67. Saad W, Han Z, Poor HV, Basar T (2012) Game-theoretic methods for the smart grid: An overview of microgrid systems, demand-side management, and smart grid communications. IEEE Signal Process Mag 29(5):86–105, ISSN 1053-5888. https://doi.org/10.1109/MSP.2012.2186410

  68. Wang Y, Saad W, Han Z, Poor HV, Baar T (2014) A game-theoretic approach to energy trading in the smart grid. IEEE Trans Smart Grid 5(3):1439–1450. ISSN 1949-3053. https://doi.org/10.1109/TSG.2013.2284664

  69. Parsons S, Wooldridge M (2002) Game theory and decision theory in multi-agent systems. Auton Agents Multi-Agent Syst 5(3):243–254

    Article  Google Scholar 

  70. Sanchez-Soriano J (2013) An overview on game theory applications to engineering. Int Game Theory Rev 15(03):1340019

    Article  MathSciNet  Google Scholar 

  71. Sandholm WH (2010) Population games and evolutionary dynamics. MIT Press, Cambridge, Mass

    MATH  Google Scholar 

  72. Weibull JW (1997) Evolutionary game theory. The MIT Press, London

    MATH  Google Scholar 

  73. Maynard Smith J, Price G (1973) The logic of animal conflict. Nature 246:15–18

    Article  Google Scholar 

  74. Nash JF (1950) Equilibrium points in n-person games. Proc Natl Acad Sci USA 36(1):48–49

    Article  MathSciNet  Google Scholar 

  75. Taylor PD, Jonker LB (1978) Evolutionary stable strategies and game dynamics. Math Biosci 40(1):145–156

    Article  MathSciNet  Google Scholar 

  76. Barreiro-Gomez J, Quijano N, Ocampo-Martinez C (2014) Distributed control of drinking water networks using population dynamics: Barcelona case study. In: Proceedings of the 53rd IEEE conference on decision and control (CDC). Los Angeles, USA, pp 3216–3221

    Google Scholar 

  77. Barreiro-Gomez J, Quijano N, Ocampo-Martinez C (2016) Constrained distributed optimization: a population dynamics approach. Automatica 69:101–116

    Article  MathSciNet  Google Scholar 

  78. Barreiro-Gomez J, Quijano N, Ocampo-Martinez C (2015) Distributed resource management by using population dynamics: wastewater treatment application. In: Proceedings of 2nd IEEE Colombian conference on automatic control (CCAC). Manizales, Colombia, pp 1–6

    Google Scholar 

  79. Barreiro-Gomez J, Obando G, Riaño-Briceño G, Quijano N, Ocampo-Martinez C (2015) Decentralized control for urban drainage systems via population dynamics: Bogota case study. In: Proceedings of the European control conference (ECC). Linz, Austria, pp 2431–2436

    Google Scholar 

  80. Ramirez-Jaime A, Quijano N, Riaño-Briceño G, Barreiro-Gomez J, Ocampo-Martinez C (2016) MatSWMM - an open-source toolbox for designing real-time control of urban drainage systems. Environ Model Softw 83:143–154

    Article  Google Scholar 

  81. García L, Barreiro-Gomez J, Escobar E, Téllez D, Quijano N, Ocampo-Martinez C (2015) Modeling and real-time control of urban drainage systems: a review. Adv Water Res 85:120–132

    Article  Google Scholar 

  82. Barreiro-Gomez J, Ocampo-Martinez C, Quijano N (2015c) Evolutionary-game-based dynamical tuning for multi-objective model predictive control. In: Olaru S, Grancharova A, Lobo Pereira F (eds) Developments in model-based optimization and control. Springer, Berlin, pp 115–138

    Chapter  Google Scholar 

  83. Poveda J, Quijano N (2012) Dynamic bandwidth allocation in wireless networks using a shahshahani gradient based extremum seeking control. In: Proceedings of the 6th international conference on network games, control and optimization (NetGCooP). Avignon, France, pp 44–50

    Google Scholar 

  84. Tembine H, Altman E, El-Azouzi R, Hayel Y (2010) Evolutionary games in wireless networks. IEEE Trans Syst Man Cybern Part B: Cybern 40(3):634–646

    Article  Google Scholar 

  85. Bomze I, Pelillo M, Stix V (2000) Approximating the maximum weight clique using replicator dynamics. IEEE Trans Neu Netw 11(6):1228–1241

    Article  Google Scholar 

  86. Pashaie A, Pavel L, Damaren CJ (2017) A population game approach for dynamic resource allocation problems. Int J Control 90(9):1957–1972. https://doi.org/10.1080/00207179.2016.1231422

    Article  MathSciNet  MATH  Google Scholar 

  87. Ramirez-Llanos E, Quijano N (2010) A population dynamics approach for the water distribution problem. Int J Control 83:1947–1964

    Article  MathSciNet  Google Scholar 

  88. Abass AAA, Hajimirsadeghi M, Mandayam NB, Gajic Z (2016) Evolutionary game theoretic analysis of distributed denial of service attacks in a wireless network. In: Proceedings of the 2016 annual conference on information science and systems (CISS). Princeton, USA, pp 36–41. https://doi.org/10.1109/CISS.2016.7460473

  89. Sandholm W (2002) Evolutionary implementation and congestion pricing. Rev Econ Stud 69(3):667–689

    Article  MathSciNet  Google Scholar 

  90. Mojica-Nava E, Macana CA, Quijano N (2014) Dynamic population games for optimal dispatch on hierarchical microgrid control. IEEE Trans Syst Man Cybern: Syst 44(3):306–317

    Article  Google Scholar 

  91. Pantoja A, Quijano N (2011) A population dynamics approach for the dispatch of distributed generators. IEEE Trans Ind Electron 58(10):4559–4567

    Article  Google Scholar 

  92. Barreiro-Gomez J, Ocampo-Martinez C, Bianchi F, Quijano N (2015d) Model-free control for wind farms using a gradient estimation-based algorithm. In: Proceedings of the European control conference (ECC). Linz, Austria, pp 1516–1521

    Google Scholar 

  93. Li N, Marden JR (2013) Designing games for distributed optimization. IEEE J Select Top Signal Process 7(2):230–242. (Special issue on adaptation and learning over complex networks)

    Google Scholar 

  94. Marden JR, Ruben SD, Pao LY (2013) A Model-Free Approach to Wind Farm Control Using Game Theoretic Methods. IEEE Trans Control Syst Technol 21(4):1207–1214

    Article  Google Scholar 

  95. Obando G, Pantoja A, Quijano N (2014) Building Temperature Control based on Population Dynamics. IEEE Trans Control Syst Technol 22(1):404–412

    Article  Google Scholar 

  96. Poveda J, Quijano N (2015) Shahshahani gradient-like extremum seeking. Automatica 58:51–59

    Article  MathSciNet  Google Scholar 

  97. Barreiro-Gomez J, Mas I, Ocampo-Martinez C, Sánchez R (2016b) Peña, Quijano N (2016) Distributed formation control of multiple unmanned aerial vehicles over time-varying graphs using population games. In: Proceedings of the 55th IEEE conference on decision and control (CDC). Las Vegas, USA, pp 5245–5250

    Google Scholar 

  98. Hofbauer J, Sigmund K (1998) Evolutionary games and population dynamics. Cambridge University Press, Cambridge

    Book  Google Scholar 

  99. Fox MJ, Shamma JS (2013) Population games, stable games, and passivity. Games 4(4):561–583

    Article  MathSciNet  Google Scholar 

  100. Berninghaus S, Haller H (2010) Local interaction on random graphs. Games 1(3): 262–285. ISSN 2073-4336. https://doi.org/10.3390/g1030262

  101. Alós-Ferrer C, Weidenholzer S (2006) Imitation, local interactions, and efficiency. Econ Lett 93:163–168

    Article  MathSciNet  Google Scholar 

  102. Boussaton O, Cohen J (2012) On the distributed learning of Nash equilibria with minimal information. In: Proceedings of the 6th international conference on network games, control, and optimization (NetGCooP). Avignon, France, pp 30–37

    Google Scholar 

  103. Gharesifard B, Cortes J (2012) Distributed convergence to Nash equilibria by adversarial networks with directed topologies. In: Proceedings of the American control conference (ACC). Montreal, Canada, pp 5881–5886

    Google Scholar 

  104. Pantoja A, Quijano N (2012) Distributed optimization using population dynamics with a local replicator equation. In: Proceedings of the 51st IEEE conference on decision and control (CDC). Maui, Hawaii, pp 3790–3795

    Google Scholar 

  105. Barreiro-Gomez J, Obando G, Quijano N (2017) Distributed population dynamics: Optimization and control applications. IEEE Trans Syst Man Cybern: Syst 47(2):304–314

    Google Scholar 

  106. Cressman R, Křivan V, (2006) Migration dynamics for the ideal free distribution. Am Nat 168(3):384–397

    Google Scholar 

  107. Novak S, Chatterjee K, Nowak MA (2013) Density games. J Theor Biol 334(2013):26–34

    Article  MathSciNet  Google Scholar 

  108. Owen G (1995) Game theory. Academic Press, Cambridge. ISBN 9780125311519

    MATH  Google Scholar 

  109. Shapley LS (1953) A value for n-person games. Ann Math Stud 28:307–317

    MathSciNet  MATH  Google Scholar 

  110. Owen G, Shapley LS (1989) Optimal location of candidates in ideological space. Int J Game Theory 18(3):339–356

    Article  MathSciNet  Google Scholar 

  111. Pérez-Castrillo D, Wettstein D (2006) An ordinal shapley value for economic environments. J Econ Theory 127(1):296–308

    Article  MathSciNet  Google Scholar 

  112. Maestre JM, Muñoz de la Peña D, Jiménez Losada A, Algaba E, Camacho EF (2014) A coalitional control scheme with applications to cooperative game theory. Opt Control Appli Methods 35:592–608

    Article  MathSciNet  Google Scholar 

  113. Muros Ponce FJ, Maestre JM, Algaba E, Alamo T, Camacho EF (2014) An iterative design method for coalitional control networks with constraints on the Shapley value. In: Proceedings of the 19th IFAC world congress. Cape Town, South Africa, pp 1188–1193

    Google Scholar 

  114. Gopalakrishnan R, Marden J, Wierman A (2011) Characterizing distribution rules for cost sharing games. Proceeding of the 5th international conference on network games, control and optimization (NetGCooP). France, Paris, pp 1–4

    Google Scholar 

  115. Khan MA, Tembine H, Vasilakos AV (2012) Evolutionary coalitional games: design and challenges in wireless networks. IEEE Wirel Commun 19(2):50–56

    Article  Google Scholar 

  116. Deng X, Papadimitriou CH (1994) On the complexity of cooperative solution concepts. Math Oper Res 19(2):257–266

    Article  MathSciNet  Google Scholar 

  117. Sandholm WH, Dokumaci E, Lahkar R (2008) The projection dynamic and the replicator dynamic. Games Econ Behav 64:666–683

    Article  MathSciNet  Google Scholar 

  118. Smith MJ (1984) The stability of a dynamic model of traffic assignment-an application of a method of lyapunov. Transp Sci 18(3):245–252

    Article  MathSciNet  Google Scholar 

  119. Lahkar R, Sandholm WH (2008) The projection dynamic and the geometry of population games. Games Econ Behav 64(2):565–590

    Article  MathSciNet  Google Scholar 

  120. Ferraioli D (2013) Logit dynamics: a model for bounded rationality. ACM SIGecom Exch 12(1):34–37

    Article  MathSciNet  Google Scholar 

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Barreiro-Gomez, J. (2019). Literature Review and Background. In: The Role of Population Games in the Design of Optimization-Based Controllers. Springer Theses. Springer, Cham. https://doi.org/10.1007/978-3-319-92204-1_2

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