Shahshahani gradient-like extremum seeking

Poveda, Jorge I.; Quijano, Nicanor

doi:10.1016/j.automatica.2015.05.002

Cited by 54 publications

(42 citation statements)

References 22 publications

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“…Besides, it is worth noting that the application of population dynamics is not limited to static optimization. Indeed, these dynamics have been used to design time-varying adaptive controllers based on extremum seeking architectures [29], [25]. Once the preliminary concepts of population dynamics have been introduced as well as the properties exhibited by these game-theoretical approaches, three applications within the context of smart cities are presented and discussed.…”

Section: Convergence To Nash Equilibriamentioning

confidence: 99%

“…Convergence properties of population games have been exploited in engineering applications during the last few years (see [29], [30] and references therein). This fact is mainly given since there exists an equivalence between the Nash equilibria of a full-potential game and the arguments that maximize its potential function.…”

Section: Convergence To Nash Equilibriamentioning

confidence: 99%

“…In the case of distributed population dynamics, additional conditions are required. These conditions are related to the location of the Nash equilibrium inside the simplex ∆, and the connectivity of the graph that models the strategy-constrained interactions of the individuals within the population.Convergence properties of population games have been exploited in engineering applications during the last few years (see [29], [30] and references therein). This fact is mainly given since there exists an equivalence between the Nash equilibria of a full-potential game and the arguments that maximize its potential function.…”

mentioning

confidence: 99%

“…Some applications of EGT in engineering problems can be found along different fields. For instance, wind farms control [20], [21], multiple access control for communication systems [22], cyber security [23], combinatorial optimization [24], bandwidth allocation [25], hierarchical frequency control in microgrids [26], dispatch of electric generators [27], building temperature control [28], constrained extremum seeking [29], and control of drinking water networks [30], among others.There are three main advantages of using EGT in engineering problems. The first motivation is that the analogy between games and engineering problems is straightforward in a large variety of cases.…”

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confidence: 99%

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The Role of Population Games and Evolutionary Dynamics in Distributed Control Systems: The Advantages of Evolutionary Game Theory

et al. 2017

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Recently, there has been an increasing interest in studying large-scale distributed systems in the control community. Efforts have been invested in developing several techniques wishing to address the main challenges found in this kind of problems, for instance, the amount of information to guarantee the proper operation of the system and the economic costs associated to the required communication structure. Moreover, another issue appears when there is a large amount of required data to control the system. The measuring and transmission processes, and the computation of the control inputs make closed-loop systems suffer from high computational burden.One way to overcome such problems is to consider the use of multi-agent systems framework, which may be cast in game-theoretical terms. Game theory studies interactions between self-interested agents. Particularly, this theory tackles with the problem of interaction between agents using different strategies that wish to maximize their welfares. For instance in [1], the authors provide connections between games, optimization, and learning for signal processing in networks. Other approaches in terms of learning and games can be found in [2]. In [3], distributed computation algorithms are developed based on generalized convex games that do not require full information, and where there is a dynamic change in terms of network topologies. Applications of game theory in control of optical networks and game-theoretic methods for smart grids are described in [4], [5], [6]. Another approach in game theoretical methods is to design protocols or mechanisms that possess some desirable properties [7]. This approach leads to a broad analysis of multi-agent interactions, particularly those involving negotiation and coordination problems [8]. Other game-theoretical applications to engineering are reported in [9].From a game-theoretical perspective, it can be distinguished among three types of games: matrix games, continuous games, and differential/dynamic games (see "The relationship among matricial games, full-potential population games, and resource allocation problems"). In matrix games (that generally use the normal form), individuals play simultaneously and only once, and the decision is mainly based on a static terms. In this case, players or agents are individually treated. Differently, in continuous games, players have infinitely many pure strategies [10], [11]. On the other hand, in dynamic games it is assumed that players can use some type of learning mechanism that allows them to adjust the actions taken based on their past decisions. Basically, it can be said that dynamic games are characterized by three main problems: i) how to model the environment where players interact; ii) how to model the objectives of players; and iii) how to specify the order in which the actions are taken and how much information each player possesses. In this case, it is assumed that there are interactions between large number of agents (which are usually unknown) [12].Among these dynamic game...

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Section: Convergence To Nash Equilibriamentioning

confidence: 99%

Section: Convergence To Nash Equilibriamentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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The Role of Population Games and Evolutionary Dynamics in Distributed Control Systems: The Advantages of Evolutionary Game Theory

et al. 2017

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“…Game-theoretical properties have been used in order to solve distributed constrained optimization problems [2], to perform optimization based on extremum seeking [10], and to solve resource allocation problems [9] [11], showing the versatility of this gametheoretical approach. Furthermore, in [1], the distributed population dynamics were presented with their corresponding stability properties.…”

Section: Introductionmentioning

confidence: 99%

Distributed formation control of multiple unmanned aerial vehicles over time-varying graphs using population games

Barreiro-Gómez

Mas

Ocampo‐Martínez

et al. 2016

2016 IEEE 55th Conference on Decision and Control (CDC)

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Abstract-This paper presents a control technique based on distributed population dynamics under time-varying communication graphs for a multi-agent system structured in a leaderfollower fashion. Here, the leader agent follows a particular trajectory and the follower agents should track it in a certain organized formation manner. The tracking of the leader can be performed in the position coordinates x, y, and z, and in the yaw angle φ. Additional features are performed with this method: each agent has only partial knowledge of the position of other agents and not necessarily all agents should communicate to the leader. Moreover, it is possible to integrate a new agent into the formation (or for an agent to leave the formation task) in a dynamical manner. In addition, the formation configuration can be changed along the time, and the distributed populationgames-based controller achieves the new organization goal accommodating conveniently the information-sharing graph in function of the communication range capabilities of each UAV. Finally, several simulations are presented to illustrate different scenarios, e.g., formation with time-varying communication network, and time-varying formation.

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Model‐based vs data‐driven adaptive control: An overview

Benosman

2018

Adaptive Control & Signal

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In this paper, we present an overview of adaptive control by contrasting model-based approaches with data-driven approaches. Indeed, we propose to classify adaptive controllers into two main subfields, namely, model-based adaptive control and data-driven adaptive control. In each subfield, we cite monographs, survey papers, and recent research papers published in the last few years. We also include a few simple examples to illustrate some general concepts in each subfield.

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Shahshahani gradient-like extremum seeking

Cited by 54 publications

References 22 publications

The Role of Population Games and Evolutionary Dynamics in Distributed Control Systems: The Advantages of Evolutionary Game Theory

The Role of Population Games and Evolutionary Dynamics in Distributed Control Systems: The Advantages of Evolutionary Game Theory

Distributed formation control of multiple unmanned aerial vehicles over time-varying graphs using population games

Model‐based vs data‐driven adaptive control: An overview

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