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Distributed population dynamics offer a game-theoretical framework for distributed decision making. As such, the application of these methods to the control of networked systems has been widely studied in the literature. The theoretical analyses available in the literature have only considered continuous-time formulations of distributed population dynamics. However, given that modern computers can only process information in a discrete-time fashion, the practical implementation of distributed population dynamics-based methods is inevitably discrete. In consequence, it is paramount to extend the available theory to a more implementable discrete-time approach. For that reason, in this article, we provide a discrete-time analysis of a general class of distributed population dynamics, and we derive sufficient conditions on the discretization time to ensure the asymptotic stability of the discretized dynamics. To illustrate the relevance and performance of the proposed methods, we apply the developed theory to distributed optimization and control problems, including a real multirobot platform, which consider noncomplete communication networks and coupled constraints. [ABSTRACT FROM AUTHOR]

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 motivate better irrigation strategies that might take advantage of the new sources of information in the spectrum of systems and control methodologies in a more systematic way. In this connection, two open questions deserve interest: (i) How can one deal with the space–time variability of soil conditions? (ii) How can one provide robustness to an irrigation system under unexpected environmental change? In this review, the different elements of an automatic control system are described, including the mathematical modeling of the crop–soil systems, instrumentation and actuation, model identification and validation from experimental data, estimation of non-measured variables and sensor fusion, and predictive control based on crop–soil and weather models. An overview of the literature is given, and several specific examples are worked out for illustration purposes. [ABSTRACT FROM AUTHOR]

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 after more forecast information becomes available. Intra-hour, or ultra-short-term dispatch, allows microgrid operators to frequently update generators’ outputs for providing power supplies efficiently in an uncertain operating condition. However, several technical challenges such as information availability, control architecture, and computational burden make it difficult to find an optimal generation schedule at runtime. This work addresses the intra-hour economic dispatch problem by designing a closed-loop distributed model predictive control (DMPC) that reduces potential variations in the determined generation schedules. At first, optimization problems regarding the behaviors of several participants in a microgrid are formulated. Then, the procedures for achieving the real-time power balance under ramp-rate constraints are detailed. Simulation results present the efficiency of the proposed DMPC for the optimal intra-hour economic dispatch when multiple types of generation resources are considered in a microgrid. [ABSTRACT FROM AUTHOR]

The Kuramoto oscillator has been widely studied because it can model a wide variety of engineering problems. Conditions for frequency synchronisation of a network of undirected coupled Kuramoto oscillators have been well established. However, the topology that describes the interaction between oscillators may not be connected due to environmental limitations or link failures. In this work we propose a consensus-based control strategy that forces the network to follow a virtual agent with a constant frequency, and where only few agents have access to the leader state. The controller is based on the exchange of information between the oscillators through a communication network. We introduce conditions for synchronisation and phase cohesiveness with the proposed control strategy that depends on the interaction between the physical topology and the communication topology. Finally, we study two applications to illustrate concepts presented in this work: (1) frequency synchronisation in smart grids; and (2) vehicle coordination. [ABSTRACT FROM AUTHOR]

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 bound on the total amount of energy supplied to the plant. The proposed approach does not need a centralised coordinator when having a coupled constraint involving all the decision variables. The proposed methodology, which takes advantage of evolutionary game theory concepts, provides an optimal solution for the described problem. Moreover, it is shown that the methodology has plug‐and‐play features, i.e. for each already designed local MPC controller nothing changes when more sub‐systems are added/removed to/from the global constrained control problem. Furthermore, the stability analysis of the proposed DMPC scheme is presented. [ABSTRACT FROM AUTHOR]

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 projection dynamics. By applying this protocol as a complementary plug‐in to a DMPC strategy, the controllers improve the resilience against communication failures and relax the requirements of the communication network. Furthermore, a reconfiguration algorithm, which is a contingency procedure to maintain the connectivity of the network, and a discussion on the selection criteria of the information‐sharing network are also presented. In order to demonstrate the performance and advantages of the proposed approach when it is applied to a DMPC strategy, a case study of a power‐network control problem is provided. [ABSTRACT FROM AUTHOR]

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 multiagent dynamical systems). The hardware that has been designed and developed performs experiments for single or multiple robot control employing capabilities, such as locomotion, communication, perception, and autonomous decision making. We illustrate different control and system dynamics techniques using this platform. After presenting the model and navigation strategies, single-agent techniques that ensure safe and predictable navigation are shown (PID and fuzzy logic controllers). Then, two multiagent techniques are described in order to prove the versatility of the platform. The first strategy illustrates common problems that arise in nature and power systems (phase synchronization models based on pulse-coupled biological oscillators and the Kuramoto oscillator). Second, a game theoretical approach for multirobot consensus is shown. In all these cases, experimental results show the performance of the platform. [ABSTRACT FROM AUTHOR]

Recent work has studied the impact caused by attackers that compromise pricing signals used in the emerging retail electricity market and send false prices to a subset of consumers. In this paper, we extend previous work by considering a more realistic adversary model that is not arbitrarily tied to scaling and delay attacks, but that can generate any arbitrary pricing signal and show how to keep the problem tractable with a new analysis based on sensitivity functions. In addition, we extend previous work by proposing countermeasures to mitigate the negative impact of these attacks. Countermeasures include selecting parameters of the controller, designing robust control algorithms, and by detecting anomalies in the behavior of the system. [ABSTRACT FROM PUBLISHER]

Since complexity and scale of systems are continuously increasing, there is a growing interest in developing distributed algorithms that are capable to address information constraints, specially for solving optimisation and decision-making problems. In this paper, we propose a novel method to solve distributed resource allocation problems that include lower bound constraints. The optimisation process is carried out by a set of agents that use a communication network to coordinate their decisions. Convergence and optimality of the method are guaranteed under some mild assumptions related to the convexity of the problem and the connectivity of the underlying graph. Finally, we compare our approach with other techniques reported in the literature, and we present some engineering applications. [ABSTRACT FROM AUTHOR]

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 challenges for the system operator, who must prevent future changes in the network status by taking efficient decisions with available information and guarantee the system security. In this document we show that Model Predictive Control (MPC) might be appropriate to solve the economic dispatch problem when feasible future events are considered. A case study is solved from different perspectives of the system operator, which is able to consider or neglect future events. Results show that MPC efficiently solves the economic dispatch problem when considering future events and ramp rate constraints. [ABSTRACT FROM PUBLISHER]

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 communication topology in a microgrid test system. Second, they consider power losses of the networked microgrid on the distributed RD. Third, they propose an algorithm to estimate robust loss coefficients of the network considering different demand patterns through a combination of two heuristic optimisation algorithms. Finally, the uncertainties of wind power are considered as a renewable generation integration case. The simulation results show that the proposed distributed control algorithm of a microgrid is able to integrate the economic dispatch problem with frequency control considering the entire network topology. [ABSTRACT FROM AUTHOR]

We investigate the invariance properties of a class of switched systems where the value of a switching signal determines the current mode of operation (among a finite number of them) and, for each fixed mode, its dynamics are described by a Differential-Algebraic Equation (DAE). Motivated by the lack of invariance principles of switched DAE systems, we develop such principles for switched DAE systems under arbitrary and dwell-time switching. By obtaining a hybrid system model that describes the switched DAE system, we build from invariance results, for hybrid systems, the invariance principles for such switched systems. Examples are included to illustrate the results. [ABSTRACT FROM AUTHOR]

Population dynamics have been widely used in the design of learning and control systems for networked engineering applications, where the information dependency among elements of the network has become a relevant issue. Classic population dynamics (e.g., replicator, logit choice, Smith, and projection) require full information to evolve to the solution (Nash equilibrium). The main reason is that classic population dynamics are deduced by assuming well-mixed populations, which limits the applications where this theory can be implemented. In this paper, we extend the concept of population dynamics for nonwell-mixed populations in order to deal with distributed information structures that are characterized by noncomplete graphs. Although the distributed population dynamics proposed in this paper use partial information, they preserve similar characteristics and properties of their classic counterpart. Specifically, we prove mass conservation and convergence to Nash equilibrium. To illustrate the performance of the proposed dynamics, we show some applications in the solution of optimization problems, classic games, and the design of distributed controllers. [ABSTRACT FROM PUBLISHER]

We illustrate the potential of applying population games in two key related problems in microgrids management: 1) economic dispatch of active and reactive power; and 2) demand response. For the dynamic economic dispatch problem, we present a hierarchical microgrid energy management algorithm able to dispatch active and reactive power dynamically. In the second case, we use an opinion dynamics model, including the market in which the network agents interact. Opinion dynamics considers individuals who shape their beliefs based on information they receive from a subset of the society and offers tools to analyze the outcome of collective decision processes, which apparently can be considered arbitrary. We propose an opinion dynamics model including some desired characteristics, such as prominent agents and environmental incentives. [ABSTRACT FROM PUBLISHER]

Most government, industry, and academic efforts to protect the power grid have focused on information security mechanisms for preventing and detecting attacks. In addition to these mechanisms, control engineering can help improve power grid security. [ABSTRACT FROM PUBLISHER]

SUMMARY Optimal control problems for switched nonlinear systems are investigated. We propose an alternative approach for solving the optimal control problem for a nonlinear switched system based on the theory of moments. The essence of this method is the transformation of a nonlinear, nonconvex optimal control problem, that is, the switched system, into an equivalent optimal control problem with linear and convex structure, which allows us to obtain an equivalent convex formulation more appropriate to be solved by high-performance numerical computing. Consequently, we propose to convexify the control variables by means of the method of moments obtaining semidefinite programs. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In the path to the future implementation of the smart grid, microgrids are presented as a cornerstone. An efficient and optimal microgrid operation is paramount. In this paper, we present a hierarchical microgrid management system using task sharing and an evolutionary game theory based dispatch strategy as a coordination algorithm to integrate the three main control levels needed for microgrid operation, which can be considered as a distributed intelligent system. The proposed algorithm shares the total load demanded into the distributed generators efficiently, while the microgrid utility is maximized dynamically, increasing the system reliability. Some simulation results are presented to illustrate the effectiveness of the dynamic performance of the proposed approach compared with a traditional hierarchical algorithm. [ABSTRACT FROM AUTHOR]