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201

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Introduction

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October 2015 - present

February 2007 - September 2015

January 2007 - present

## Publications

Publications (201)

While humans and animals learn incrementally during their lifetimes and exploit their experience to solve new tasks, standard deep reinforcement learning methods specialize to solve only one task at a time. As a result, the information they acquire is hardly reusable in new situations. Here, we introduce a new perspective on the problem of leveragi...

This brief proposes a novel decision-making model for generalized Nash equilibrium seeking in the context of full-potential population games under capacity and migration constraints. The capacity constraints restrict the mass of players that are allowed to simultaneously play each strategy of the game, while the migration constraints introduce a ne...

This paper studies the problem of generalized Nash equilibrium seeking in population games under general affine equality and convex inequality constraints. In particular, we design a novel payoff dynamics model to steer the decision-making agents to a generalized Nash equilibrium of the underlying game, i.e., to a self-enforceable state where the c...

The smart use of water is a key factor in increasing food production. Over the years, irrigation has relied on historical data and traditional management policies. Control techniques have been exploited to build automatic irrigation systems based on climatic records and weather forecasts. However, climate change and new sources of information motiv...

To understand the impact of cyber-attacks to sensors in control systems, we present a stability analysis of a wide range of systems in this paper. Based on Lyapunov stability analysis, we formulate an optimization problem with constraints in the form of a set of linear matrix inequalities to find conservative bounds of stability related to the atta...

This paper proposes a decentralized control scheme that mitigates floods in urban drainage systems (UDSs). First, we develop a partitioning algorithm of the UDS relying on a graph model of the system. Once this is done, we design a local controller for each partition based on the replicator dynamics model (a set of differential equations that descr...

As an envisioned technology for future smart city networks, this paper studies the real-time decentralized charging coordination of a fleet of plug-in electric vehicles (PEVs) under feeder capacity constraints. In particular, inspired by some ideas in the field of population games and payoff dynamics models, we propose a novel form of continuous-ti...

The development of modeling and estimation strategies, useful for determining the magnitude and location of unknown flows such as seepage and leaks, appears as a valuable tool to increase the efficiency of the open-channel irrigation systems (OCIS). However, it has been identified that in OCIS, most of the strategies reported on detection, isolatio...

We study the problem of minimizing a convex function over probability measures supported in a graph. We build upon the recent formulation of optimal transport over discrete domains to propose a method that generates a sequence that provably converges to a minimum of the objective function and smoothly transports mass over the edges of the graph. Mo...

We study the problem of minimizing a convex function over probability measures supported in a graph. We build upon the recent formulation of optimal transport over discrete domains to propose a method that generates a sequence that provably converges to a minimum of the objective function and smoothly transports mass over the edges of the graph. Mo...

Power systems operation has been traditionally addressed by deterministic and centralized approaches because of their low-variation behavior. However, current tendencies have introduced variability and stochasticity as a result of including renewable energy sources, active demand participation, and short-term market clearing. Thereby, operators are...

This brief proposes a novel form of continuous-time evolutionary game dynamics for generalized Nash equilibrium seeking in equality-constrained population games. Using Lyapunov stability theory and duality theory, we provide sufficient conditions to guarantee the asymptotic stability, non-emptiness, compactness, and optimality of the equilibria set...

This work proposes a novel data-driven distributed formation-control approach based on multi-population evolutionary games, which is structured in a leader-follower scheme. The methodology considers a time-varying communication graph that describes how the multiple agents share information to each other. We present stability guarantees for configur...

In recent years the number of security incidents affecting control systems has increased. These incidents have shown the need to develop strategies to improve system resilience to cyber-attacks. This paper presents a practical implementation of a strategy to detect cyber-attacks and mitigate their effects on sensors of a multi-agent system. The pro...

This work proposes a distributed strategy to solve a joint active and reactive power dispatch in isolated microgrids. The information shared among neighboring local controllers allows the algorithm to achieve the optimal dispatch in a secondary layer, which is the reference for primary controllers in each generator. The method based on population d...

Water is the most important element of food production, and the easiest and most cost-efficient way to transport it is through open-channel irrigation systems (OCIS). These types of systems have a high agricultural and ecological impact. However, in most countries, OCIS lack automation and efficiency at mitigating the economic and environmental cos...

Climate change and the efficient use of freshwater for irrigation pose a challenge for sustainable agriculture. Traditionally, the prediction of agricultural production is carried out through crop-growth models and historical records of the climatic variables. However, one of the main flaws of these models is that they do not consider the variabili...

El objetivo central del libro blanco que se presenta a continuación es mostrar un camino para contar con una estrategia efectiva para la reducción de la huella de carbono de las ciudades y hacer frente a los impactos y riesgos del cambio climático. Se analizan las políticas, regulaciones y capacidades requeridas para que los proyectos urbanos pueda...

The majority of crop growth models assume homogeneous soil and crop components where state variables only depend on time and not on space. However, in view of increasing crop yield by efficient irrigation, spatial variability of soil state variables (e.g. soil water content) should be considered. Recent models reported in the literature include the...

Population games can be regarded as a tool to study the strategic interaction of a population of players. Although several attention has been given to such field, most of the available works have focused only on the unconstrained case. That is, the allowed equilibrium of the game is not constrained. To further extend the capabilities of population...

We present a new class of accelerated distributed algorithms for the robust solution of convex optimization problems over networks. The novelty of the approach lies in the introduction of
distributed restarting mechanisms
that coordinate the evolution of accelerated optimization dynamics with individual asynchronous and periodic time-varying mome...

High penetration of distributed generation will be characteristic to future distribution networks. The dynamic, intermittent, uncertain and deregulated nature of distributed generation raises the need for online, distributed economic dispatch techniques. In this paper, we demonstrate the application of such approaches using population dynamics. We...

Document available at arxiv.
Deep reinforcement learning techniques have shown to be a promising path to solve very complex tasks that once were thought to be out of the realm of machines. However, while humans and animals learn incrementally during their lifetimes and exploit their experience to solve new tasks, standard deep learning methods spe...

This paper investigates the combination of reinforcement learning and neural networks applied to the data-driven control of dynamical systems. In particular, we propose a multi-critic actor-critic architecture that eases the value function learning task by distributing it into multiple neural networks. We also propose a filtered multi-critic approa...

This paper studies the distributed formation control of multiple differential-drive robots. To solve such problem, we propose a novel class of distributed population dynamics, formulated in discrete-time, and we obtain sufficient conditions to guarantee asymptotic stability in practical implementations where computations are necessarily discrete. M...

In irrigation, most of the water is transported by networks of open-channel irrigation systems (OCIS). In most cases, the OCIS are manually operated showing low efficiency. Then the incorporation of control strategies is one of the most practical ways to increase the efficiency of these systems. However, in order to design an appropriate control st...

Distributed generation entities such as renewable energy sources have posed great challenges on power system economic dispatch because of their output variability and stochasticity. Accordingly, operators need to lessen unpredictable changes in scheduled generation settings by fully utilizing available forecast information in the decision-making pr...

Renewable energy-based generation facilities emerging in microgrids are modifying many traditional principles of economic dispatch because of the variability and uncertainty of their output characteristics. Since the power generation from renewable resources is difficult to anticipate, a real-time adjustment of generation schedules is necessary aft...

Consensus algorithms are widely used in control theory to design distributed control techniques in networked dynamical systems. One of the most conventional techniques is model predictive control (MPC) because it can handle restrictions over the states and system inputs. However, the computational resources required by the algorithm could make the...

Renewable energy sources, active demand participation, and prices fluctuation have introduced variability and stochasticity in power systems operation. Accordingly, operators are looking for utilizing available forecast information in order to enhance the system response to unpredictable changes. This paper considers a data-driven scenario generati...

The modernization of the power system introduces technologies that may improve the system’s efficiency by enhancing the capabilities of users. Despite their potential benefits, such technologies can have a negative impact. This subject has widely analyzed, mostly considering for-profit electric utilities. However, the literature has a gap regarding...

This paper describes how to design and implement a mechanism that helps to mitigate sensor attacks on industrial control systems. The proposed architecture is based on concepts from fault-tolerant control techniques. This short note explains how a Kalman filter can be used simultaneously with optimal disturbance decoupling observers to improve the...

The modernization of the power system introduces technologies that may improve the system's efficiency by enhancing the capabilities of users. Despite their potential benefits, such technologies can have a negative impact. This subject has widely analyzed, mostly considering for-profit electric utilities. However, the literature has a gap regarding...

A hierarchical approach for the energy management of geographically close microgrids connected through a dedicated AC power network is proposed in this paper. The proposed approach consists of a two-layer energy management system (EMS) for networked microgrids. In the lower layer, each microgrid solves its own economic dispatch problem through a di...

Renewable energy-based generation facilities emerging in microgrids are modifying many traditional principles of economic dispatch because of the variability and uncertainty of their output characteristics. Since the power generation from renewable resources is difficult to anticipate, a real-time adjustment of generation schedules is necessary aft...

This study proposes a distributed model predictive control (DMPC) scheme based on population games for a system formed by a set of sub-systems. In addition to considering independent operational constraints for each sub-system, the controller addresses a coupled constraint that involves the sum of all control inputs. This constraint models an upper...

In wind farms, the interaction between turbines that operate close by experience some problems in terms of their power generation. Wakes caused by upstream turbines are mainly responsible of these interactions, and the phenomena involved in this case is complex especially when the number of turbines is high. In order to deal with these issues, ther...

The design of distributed optimization-based controllers for large-scale systems (LSSs) implies every time new challenges. The fact that LSSs are generally located throughout large geographical areas makes difficult the recollection of measurements and their transmission. In this regard, the communication network that is required for a centralized...

The integration of modern information technologies with industrial control systems has created an enormous interest in the security of industrial control, however, given the cost, variety, and industry practices, it is hard for researchers to test and deploy security solutions in real-world systems. Industrial control testbeds can be used as tools...

In a multi-agent framework, distributed optimization problems are generally described as the minimization of a global objective function, where each agent can get information only from a neighborhood defined by a network topology. To solve the problem, this work presents an information-constrained strategy based on population dynamics, where payoff...

Information sharing among local controllers is the key feature of any distributed model predictive control (DMPC) strategy. This study addresses the problem of communication failures in DMPC strategies and proposes a distributed solution to cope with them. The proposal consists in an information-exchange protocol that is based on distributed projec...

The development of an inexpensive robotic platform is presented. Our aim is to provide a complete low-cost hardware tool to illustrate control system methods. The capabilities of this platform allow both, researchers and students, to implement from basic techniques to complex ones (e.g., from model-based control techniques to multi-agent dynamical...

Presents information on the 2017 Manufacturing Automation and Robotic Control.

The Kuramoto oscillator has been widely studied because it can model a wide variety of biological, social, chemical, and engineering problems. Conditions for frequency synchronization of a network of undirected coupled Kuramoto oscillators have been well established and they depend on the connectivity of the physical network. Typically, the frequen...

Distributed Model Predictive Control (DMPC) strategies require local controllers to share information among each other. Considering the importance of communication in such control strategies and the failures that may occur in the information-sharing network, this paper proposes to apply the distributed consensus algorithm as an information-exchange...

In Colombia, there is an increasing interest about improving public
transportation. One of the proposed strategies in that way is the use battery
electric vehicles (BEVs). One of the new challenges is the BEVs routing
problem, which is subjected to the traditional issues of the routing problems,
and must also consider the particularities of autonom...

This paper solves a data-driven control problem for a flow-based distribution network with two objectives: a resource allocation and a fair distribution of costs. These objectives represent both cooperation and competition directions. It is proposed a solution that combines either a centralized or distributed cooperative game approach using the Sha...

A weakness of most distributed resource allocation algorithms proposed in the literature is that they assume that all nodes of the underlying graph are involved in the resource allocation problem. Such assumption does not hold in some applications. This manuscript deals with the problem of resource allocation among a subset of nodes of a graph. We...

Population games have become a powerful tool for solving resource-allocation problems in a distributed manner, and for the design of non-centralized optimization-based controllers. The aim of this paper is to illustrate the advantages of two recently introduced population-game approaches in comparison to other classical optimization methods. More s...

Large-scale systems involve a high number of variables making challenging the design of controllers because of information availability and computational burden issues. Normally, the measurement of all the states in a large-scale system implies to have a big communication network, which might be quite expensive. On the other hand, the treatment of...

This work addresses the design of a control strategy for drinking-water transport networks (DWTNs) based on evolutionary-game theory
(EGT). This theory allows to model the evolution of a population composed by a large and finite number of rational agents, which are able to make decisions. As an analogy with a multi-variable control system for DWTN,...

A distributed population games algorithm is proposed to solve the dispatch problem in microgrids to respond dynamically to the requirements of the system. This study extends the distributed replicator dynamics (RD) algorithm, it has four main contributions. First, the authors apply the distributed RD to a distributed generator dispatch over a commu...

Model predictive control (MPC) is a suitable strategy for the control of large-scale systems that have multiple design requirements, e.g., multiple physical and operational constraints. Besides, an MPC controller is able to deal with multiple control objectives considering them within the cost function, which implies to determine a proper prioritiz...

Power system operation has been a very complex and interesting problem, and currently has gathered more attention because of new elements in the network. New elements such as renewable sources, demand response, electric vehicles, and energy storage systems, are crucial because of their stochastic behavior. Stochastic variables present new challenge...

There has been increasing interest in the control community in studying large-scale distributed systems, and numerous techniques have been developed to address the main challenges in these problems. One way to approach these types of problems is to use a multiagent systems framework, which can be cast in game-theoretical terms. Game theory has trad...

Recently, there has been an increasing interest in the control community in studying large-scale distributed systems. Several techniques have been developed to address the main challenges for these systems, such as the amount of information needed to guarantee the proper operation of the system, the economic costs associated with the required commu...