Showing papers on "Multi-agent system published in 2015"
TL;DR: A distributed adaptive consensus protocol is designed to achieve leader-follower consensus in the presence of a leader with a zero input for any communication graph containing a directed spanning tree with the leader as the root node.
Abstract: This technical note addresses the distributed consensus protocol design problem for multi-agent systems with general linear dynamics and directed communication graphs. Existing works usually design consensus protocols using the smallest real part of the nonzero eigenvalues of the Laplacian matrix associated with the communication graph, which however is global information. In this technical note, based on only the agent dynamics and the relative states of neighboring agents, a distributed adaptive consensus protocol is designed to achieve leader-follower consensus in the presence of a leader with a zero input for any communication graph containing a directed spanning tree with the leader as the root node. The proposed adaptive protocol is independent of any global information of the communication graph and thereby is fully distributed. Extensions to the case with multiple leaders are further studied.
799 citations
TL;DR: This paper investigates the fixed-time consensus tracking problem for second-order multi-agent systems in networks with directed topology with a proposed framework that eliminates the singularity and the settling time is assignable for any initial conditions.
716 citations
TL;DR: The problem of second-order leader-following consensus by a novel distributed event-triggered sampling scheme in which agents exchange information via a limited communication medium is studied and it is shown that the inter-event intervals are lower bounded by a strictly positive constant, which excludes the Zeno-behavior before the consensus is achieved.
Abstract: In this note, the problem of second-order leader-following consensus by a novel distributed event-triggered sampling scheme in which agents exchange information via a limited communication medium is studied. Event-based distributed sampling rules are designed, where each agent decides when to measure its own state value and requests its neighbor agents broadcast their state values across the network when a locally-computed measurement error exceeds a state-dependent threshold. For the case of fixed topology, a necessary and sufficient condition is established. For the case of switching topology, a sufficient condition is obtained under the assumption that the time-varying directed graph is uniformly jointly connected. It is shown that the inter-event intervals are lower bounded by a strictly positive constant, which excludes the Zeno-behavior before the consensus is achieved. Numerical simulation examples are provided to demonstrate the correctness of theoretical results.
521 citations
TL;DR: It is shown that continuous communication between neighboring agents can be avoided and the Zeno-behavior of triggering time sequences is excluded and a numerical example is presented to illustrate the effectiveness of the obtained theoretical results.
Abstract: The event-based control strategy is an effective methodology for tackling the distributed control of multi-agent systems with limited on-board resources. This technical note focuses on event-based leader-following consensus for multi-agent systems described by general linear models and subject to input time delay between controller and actuator. For each agent, the controller updates are event-based and only triggered at its own event times. A necessary condition and two sufficient conditions on leader-following consensus are presented, respectively. It is shown that continuous communication between neighboring agents can be avoided and the Zeno-behavior of triggering time sequences is excluded. A numerical example is presented to illustrate the effectiveness of the obtained theoretical results.
379 citations
TL;DR: For the first time, GFHMs are used to approximate the solutions (value functions) of the coupled HJ equations, based on policy iteration algorithm, and the approximation solution is utilized to obtain the optimal coordination control.
Abstract: In this paper, a new online scheme is presented to design the optimal coordination control for the consensus problem of multiagent differential games by fuzzy adaptive dynamic programming, which brings together game theory, generalized fuzzy hyperbolic model (GFHM), and adaptive dynamic programming. In general, the optimal coordination control for multiagent differential games is the solution of the coupled Hamilton-Jacobi (HJ) equations. Here, for the first time, GFHMs are used to approximate the solutions (value functions) of the coupled HJ equations, based on policy iteration algorithm. Namely, for each agent, GFHM is used to capture the mapping between the local consensus error and local value function. Since our scheme uses the single-network architecture for each agent (which eliminates the action network model compared with dual-network architecture), it is a more reasonable architecture for multiagent systems. Furthermore, the approximation solution is utilized to obtain the optimal coordination control. Finally, we give the stability analysis for our scheme, and prove the weight estimation error and the local consensus error are uniformly ultimately bounded. Further, the control node trajectory is proven to be cooperative uniformly ultimately bounded.
371 citations
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TL;DR: In this article, the Deep Q-Learning Network architecture was extended to multiagent environments and investigated how two agents controlled by independent deep Q-networks interact in the classic videogame Pong.
Abstract: Multiagent systems appear in most social, economical, and political situations. In the present work we extend the Deep Q-Learning Network architecture proposed by Google DeepMind to multiagent environments and investigate how two agents controlled by independent Deep Q-Networks interact in the classic videogame Pong. By manipulating the classical rewarding scheme of Pong we demonstrate how competitive and collaborative behaviors emerge. Competitive agents learn to play and score efficiently. Agents trained under collaborative rewarding schemes find an optimal strategy to keep the ball in the game as long as possible. We also describe the progression from competitive to collaborative behavior. The present work demonstrates that Deep Q-Networks can become a practical tool for studying the decentralized learning of multiagent systems living in highly complex environments.
288 citations
TL;DR: Both theoretical and numerical results show that the optimal load sharing can be achieved within both generation and delivering constraints in a distributed way.
276 citations
TL;DR: It has been proved that with the proposed “Zeno-free” algorithm the agent group can achieve consensus asymptotically and more energy can be saved using the proposed algorithm in practical multi-agent systems.
Abstract: In this technical note, a self-triggered consensus algorithm for multi-agent systems has been proposed. Each agent receives the state information of its neighbors and computes the average state of its neighborhood. Based on this average state the event trigger is designed to determine when the agent updates its control input and transmits the average state to its neighbors. By specifying a strictly positive minimal inter-event time for each agent, Zeno behavior can be avoided. Then by solving quadratic equations related to the event condition, the self-triggered consensus algorithm is developed by directly computing the event time instants with a set of iterative procedures. It has been proved that with the proposed “Zeno-free” algorithm the agent group can achieve consensus asymptotically. Compared with the existing works, the proposed algorithm is simpler in formulation and computation. Moreover, it has been showed that agents need less time to achieve consensus with considerable reduction of the number of triggering events, controller updates and information transmission. As a result, more energy can be saved using the proposed algorithm in practical multi-agent systems.
269 citations
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TL;DR: A comparative up-to-date review of the most promising existing agent platforms that can be used is presented, based on universal comparison and evaluation criteria, proposing classifications for helping readers to understand which agent platforms broadly exhibit similar properties and in which situations which choices should be made.
Abstract: From computer games to human societies, many natural and artificial phenomena can be represented as multi-agent systems. Over time, these systems have been proven a really powerful tool for modelling and understanding phenomena in fields, such as economics and trading, health care, urban planning and social sciences. However, although, intelligent agents have been around for years, their actual implementation is still in its early stages. Since the late nineties many agent platforms have been developed. Some of them have already been abandoned whereas others continue releasing new versions. On the other hand, the agent-oriented research community is still providing more and more new platforms. This vast amount of platform options leads to a high degree of heterogeneity. Hence, a common problem is how people interested in using multi-agent systems should choose which platform to use in order to benefit from agent technology. This decision was usually left to word of mouth, past experiences or platform publicity, lately however people depend on solid survey articles. To date, in most cases multi-agent system surveys describe only the basic characteristics of a few representatives without even providing any classification of the systems themselves. This article presents a comparative up-to-date review of the most promising existing agent platforms that can be used. It is based on universal comparison and evaluation criteria, proposing classifications for helping readers to understand which agent platforms broadly exhibit similar properties and in which situations which choices should be made.
265 citations
TL;DR: A novel definition of consensus in probability is proposed to better describe the dynamics of the consensus process of the addressed stochastic multi-agent systems with state-dependent noises.
252 citations
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TL;DR: In this paper, the authors considered the consensus problem of hybrid multi-agent system and proposed three kinds of consensus protocols for HMMS, which are based on matrix theory and graph theory.
Abstract: In this paper, we consider the consensus problem of hybrid multi-agent system. First, the hybrid multi-agent system is proposed which is composed of continuous-time and discrete-time dynamic agents. Then, three kinds of consensus protocols are presented for hybrid multi-agent system. The analysis tool developed in this paper is based on the matrix theory and graph theory. With different restrictions of the sampling period, some necessary and sufficient conditions are established for solving the consensus of hybrid multi-agent system. The consensus states are also obtained under different protocols. Finally, simulation examples are provided to demonstrate the effectiveness of our theoretical results.
TL;DR: A distributed fault-tolerant leader-follower consensus protocol for multi-agent system is constructed by the proposed adaptive method and a simulation example is given to illustrate the effectiveness of the theoretical analysis.
Abstract: In this paper, fault-tolerant consensus in multi-agent system using distributed adaptive protocol is investigated. Firstly, distributed adaptive online updating strategies for some parameters are proposed based on local information of the network structure. Then, under the online updating parameters, a distributed adaptive protocol is developed to compensate the fault effects and the uncertainty effects in the leaderless multi-agent system. Based on the local state information of neighboring agents, a distributed updating protocol gain is developed which leads to a fully distributed continuous adaptive fault-tolerant consensus protocol design for the leaderless multi-agent system. Furthermore, a distributed fault-tolerant leader–follower consensus protocol for multi-agent system is constructed by the proposed adaptive method. Finally, a simulation example is given to illustrate the effectiveness of the theoretical analysis.
TL;DR: It is proved that the exponential consensus of the multi-agent system can be achieved with the proposed consensus protocol and a simulation example is given to illustrate the effectiveness of the proposed protocol.
TL;DR: This work modify the conventional output regulation error in such a way that it can handle more than one leader, and introduces a dynamic compensator, based on a new formulation for containment error.
TL;DR: This paper will review MAS concepts, architectures, develop platforms and processes, provide example applications, and discuss limitations, to survey applications of MAS in the control and operation of microgrids.
26 Jul 2015
TL;DR: In this paper, a multi-agent system (MAS) is implemented in a microgrid energy market implemented in JADE for scheduling, coordination and market clearing subject to system, DG and load constraints.
Abstract: In market operations, Distributed Generators (DGs) and price-sensitive loads participate in a microgrid energy market implemented in JADE Each DG and each price-sensitive load is represented by the respective agents which perform various functions such as scheduling, coordination and market clearing subject to system, DG and load constraints Each agent is assigned to one of the several agent objectives which maximizes either DG or load surpluses or both In simulated operation of a microgrid, hourly power reference signals and load control signals from JADE are passed to DG and load models developed in MATLAB/Simulink using MACSimJX Simulated operation of DGs and loads are studied by performing simulations under different agent objectives Results from simulation studies demonstrate the effectiveness of implementing multi agent system (MAS) in the distributed management of microgrids
TL;DR: A two-dimensional self-organization mechanism was designed taking the behavioural and structural vectors into consideration, thus allowing truly evolutionary and reconfigurable systems to be achieved that can cope with emergent requirements.
TL;DR: It is shown that the proposed method is user friendly in that there is no need for detail dynamic information of the agent or costly detection/diagnosis of the actuation faults in control design and implementation, resulting in a structurally simple and computationally inexpensive solution for the leaderless consensus problem of MAS.
Abstract: This paper studies the distributed consensus problem of multiagent systems (MASs) in the presence of nonidentical unknown nonlinear dynamics and undetectable actuation failures. Of particular interest is the development of a robust adaptive fault-tolerant consensus protocol capable of compensating uncertain dynamics/disturbances and time-varying yet unpredictable actuation failures simultaneously. By introducing the virtual parameter estimation error into the artfully chosen Lyapunov function, the consensus problem is solved with a robust adaptive fault-tolerant control scheme based upon local (neighboring) agent state information. It is shown that the proposed method is user friendly in that there is no need for detail dynamic information of the agent or costly detection/diagnosis of the actuation faults in control design and implementation, resulting in a structurally simple and computationally inexpensive solution for the leaderless consensus problem of MAS. Simulation results illustrate and verify the benefits and effectiveness of the proposed scheme.
TL;DR: An IOT infrastructure for collaborative warehouse order fulfillment based on RFID, ambient intelligence and multi-agent system that integrates a bottom-up approach with decision support mechanisms such as self-organization and negotiation protocols between agents based on "com-peration=competition+cooperation" concept is proposed.
TL;DR: It is proved that the proposed protocol can solve the state consensus problem when the topology is connected, and also can reduce the controller-updating costs and communication costs.
Abstract: This technical note studies the consensus problem of multiple double-integrator networks under undirected topologies. A consensus protocol based on sampled-data control and edge event-driven techniques is proposed, and two associated event-triggering rules are presented. These edge events rely on the information of the corresponding two neighboring agents, and the event-triggering actions over edges are independent of each other. It is proved that the proposed protocol can solve the state consensus problem when the topology is connected, and also can reduce the controller-updating costs and communication costs. Finally, simulations are given to demonstrate the effectiveness of our theoretical results.
TL;DR: In this paper, a decentralized approach for solving the economic dispatch problem consists of either two or three stages, where a flooding-based consensus algorithm is proposed in order to achieve consensus among the agents with respect to the units and system data.
Abstract: A new decentralized approach for solving the economic dispatch problem is presented in this paper. The proposed approach consists of either two or three stages. In the first stage, a flooding-based consensus algorithm is proposed in order to achieve consensus among the agents with respect to the units and system data. In the second stage, a suitable algorithm is used for solving the economic dispatch problem in parallel. For cases in which a nondeterministic method is used in the second stage, a third stage is applied to achieve consensus about the final solution of the problem, with a flooding-based consensus algorithm for sharing the information required during this stage. The proposed approach is highly effective for solving the non-convex formulation of the economic dispatch problem and for incorporating transmission losses accurately in a fully decentralized manner. Three case studies that were examined for validation purposes are described. The results obtained demonstrate that the proposed approach aggregates many of the advantages of both centralized and fully decentralized mechanisms for solving the economic dispatch problem.
TL;DR: This paper first study the distributed containment control problem for multiple second-order nonlinear systems with multiple dynamic leaders in the presence of unknown nonlinearities and external disturbances under a general directed graph that characterizes the interaction among the leaders and the followers.
Abstract: In this paper, we consider the distributed containment control problem for multiagent systems with unknown nonlinear dynamics. More specifically, we focus on multiple second-order nonlinear systems and networked Lagrangian systems. We first study the distributed containment control problem for multiple second-order nonlinear systems with multiple dynamic leaders in the presence of unknown nonlinearities and external disturbances under a general directed graph that characterizes the interaction among the leaders and the followers. A distributed adaptive control algorithm with an adaptive gain design based on the approximation capability of neural networks is proposed. We present a necessary and sufficient condition on the directed graph such that the containment error can be reduced as small as desired. As a byproduct, the leaderless consensus problem is solved with asymptotical convergence. Because relative velocity measurements between neighbors are generally more difficult to obtain than relative position measurements, we then propose a distributed containment control algorithm without using neighbors’ velocity information. A two-step Lyapunov-based method is used to study the convergence of the closed-loop system. Next, we apply the ideas to deal with the containment control problem for networked unknown Lagrangian systems under a general directed graph. All the proposed algorithms are distributed and can be implemented using only local measurements in the absence of communication. Finally, simulation examples are provided to show the effectiveness of the proposed control algorithms.
TL;DR: The leader-following consensus problem of fractional-order multi-agent systems is considered and the control of each agent using local information is designed and detailed analysis of the leader- following consensus is presented.
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TL;DR: In this article, the authors propose an IoT infrastructure for collaborative warehouse order fulfillment based on RFID, ambient intelligence and multi-agent system, which integrates a bottom-up approach with decision support mechanisms such as self-organization and negotiation protocols between agents based on "comperation=competition+cooperation" concept.
Abstract: Industrial deployment of the Internet Of Things (IOT) provides development of an ideal platform for decentralized management of warehouses. In this paper, we propose an IOT infrastructure for collaborative warehouse order fulfillment based on RFID, ambient intelligence and multi-agent system. It consists of a physical devices layer, a middleware ambient platform, a multi-agent system and an enterprise resource planning. It integrates a bottom-up approach with decision support mechanisms such as self-organization and negotiation protocols between agents based on "com-peration=competition+cooperation" concept. This approach was selected to improve reaction capabilities of decentralized management of warehouses in a dynamic environment. A collaborative warehouse example was conducted to demonstrate the implementation of the proposed infrastructure.
TL;DR: This work proposes a design pattern for collective decision making grounded on experimental/theoretical studies of the nest-site selection behaviour observed in honeybee swarms (Apis mellifera), and provides formal guidelines for the microscopic implementation of collective decisions to quantitatively match the macroscopic predictions.
Abstract: The engineering of large-scale decentralised systems requires sound methodologies to guarantee the attainment of the desired macroscopic system-level behaviour given the microscopic individual-level implementation. While a general-purpose methodology is currently out of reach, specific solutions can be given to broad classes of problems by means of well-conceived design patterns. We propose a design pattern for collective decision making grounded on experimental/theoretical studies of the nest-site selection behaviour observed in honeybee swarms (Apis mellifera). The way in which honeybee swarms arrive at consensus is fairly well-understood at the macroscopic level. We provide formal guidelines for the microscopic implementation of collective decisions to quantitatively match the macroscopic predictions. We discuss implementation strategies based on both homogeneous and heterogeneous multiagent systems, and we provide means to deal with spatial and topological factors that have a bearing on the micro-macro link. Finally, we exploit the design pattern in two case studies that showcase the viability of the approach. Besides engineering, such a design pattern can prove useful for a deeper understanding of decision making in natural systems thanks to the inclusion of individual heterogeneities and spatial factors, which are often disregarded in theoretical modelling.
TL;DR: The results show that the proposed agent-based local search genetic algorithm improves the efficiency.
TL;DR: The goal of this paper is to provide a comprehensive introduction to agent-based modeling for water resources researchers, students, and practitioners, and to explore water resources systems as complex adaptive systems that can be studied using agent- based modeling.
Abstract: Agent-based systems have been developed for many scientific applications and simulation studies to model a group of actors and their interactions based on behavioral rules. Agent-based models and multiagent systems simulate the emergence of system-level properties based on the actions of adaptive agents that interact with other agents, react to environmental signals, and optimize decisions to achieve individual goals. In water resources planning and management, agent-based modeling has been applied to explore, simulate, and predict the performance of infrastructure design and policy decisions as they are influenced by human decision making, behaviors, and adaptations. The goal of this paper is to provide a comprehensive introduction to agent-based modeling for water resources researchers, students, and practitioners, and to explore water resources systems as complex adaptive systems that can be studied using agent-based modeling. Agent-based modeling is defined, and the characteristics of complex ...
TL;DR: Under the assumption that the network has sufficient connectivity in terms of robustness, this work develops a resilient algorithm where each agent ignores the neighbors which have large and small position values to avoid being influenced by malicious agents.
TL;DR: This paper investigates the output consensus problem of heterogeneous discrete-time multiagent systems with individual agents subject to structural uncertainties and different disturbances and presents a novel distributed control law based on internal reference models for output consensus.
Abstract: This paper investigates the output consensus problem of heterogeneous discrete-time multiagent systems with individual agents subject to structural uncertainties and different disturbances. A novel distributed control law based on internal reference models is first presented for output consensus of heterogeneous discrete-time multiagent systems without structural uncertainties, where internal reference models embedded in controllers are designed with the objective of reducing communication costs. Then based on the distributed internal reference models and the well-known internal model principle, a distributed control law is further presented for output consensus of heterogeneous discrete-time multiagent systems with structural uncertainties. It is shown in both cases that the consensus trajectory of the internal reference models determines the output trajectories of agents. Finally, numerical simulation results are provided to illustrate the effectiveness of the proposed control schemes.
TL;DR: This paper proposes a fully distributed online OEM solution for smart grids based on a market-based self-interests motivation model since this model can realize the global social welfare maximization among system participants.
Abstract: Traditionally, economic dispatch and demand response (DR) are considered separately, or implemented sequentially, which may degrade the energy efficiency of the power grids. One important goal of optimal energy management (OEM) is to maximize the social welfare through the coordination of the suppliers’ generations and customers’ demands. Thus, it is desirable to consider the interactive operation of economic dispatch and DR, and solve them in an integrated way. This paper proposes a fully distributed online OEM solution for smart grids. The proposed solution considers the economic dispatch of conventional generators, DR of users, and operating conditions of renewable generators all together. The proposed distributed solution is developed based on a market-based self-interests motivation model since this model can realize the global social welfare maximization among system participants. The proposed solution can be implemented with multiagent system with each system participant assigned with an energy management agent. Based on the designed distributed algorithms for price updating and supply–demand mismatch discovery, the OEM among agents can be achieved in a distributed way. Simulation results demonstrate the effectiveness of the proposed solution.