Abstract: This paper studies the consensus problem of multi-agent systems with general linear dynamics. We propose a novel event-triggered control scheme with some desirable features, namely, distributed, asynchronous, and independent. It is shown that consensus of the controlled multi-agent system can be reached asymptotically. The feasibility of the event-triggered strategy is further verified by the exclusion of both singular triggering and Zeno behavior. Moreover, a self-triggered algorithm is developed, where the next triggering time instant for each agent is determined based on its local information at the previous triggering time instant. Continuous monitoring of measurement errors is thus avoided. The effectiveness of the proposed control schemes is demonstrated by two examples.

Abstract: This paper investigates the robust finite-time consensus problem of multi-agent systems in networks with undirected topology. Global nonlinear consensus protocols augmented with a variable structure are constructed with the aid of Lyapunov functions for each single-integrator agent dynamics in the presence of external disturbances. In particular, it is shown that the finite settling time of the proposed general framework for robust consensus design is upper bounded for any initial condition. This makes it possible for network consensus problems to design and estimate the convergence time offline for a multi-agent team with a given undirected information flow. Finally, simulation results are presented to demonstrate the performance and effectiveness of our finite-time protocols.

Abstract: Future industrial systems can be realized using the cyber–physical systems (CPSs) that advocate the coexistence of cyber and physical counterparts in a network structure to perform the system’s functions in a collaborative manner. Multiagent systems share common ground with CPSs and can empower them with a multitude of capabilities in their efforts to achieve complexity management, decentralization, intelligence, modularity, flexibility, robustness, adaptation, and responsiveness. This work surveys and analyzes the current state of the industrial application of agent technology in CPSs, and provides a vision on the way agents can effectively enable emerging CPS challenges.

Abstract: This technical note addresses the distributed containment control problem for a linear multi-leader multi-agent system with a directed communication topology. A new class of distributed observer-type containment protocols based only on the relative output measurements of the neighboring agents is proposed, removing the impractical assumption in some of the existing approaches that the observers embedded in the multiple dynamic agents have to share information with their neighbors. Under the mild assumption that, for each follower, there exists at least one leader having a directed path to that follower, some sufficient conditions are derived to guarantee the states of the followers to asymptotically converge to a convex hull formed by those of the dynamic leaders. Finally, some numerical simulations on containment of a multi-vehicle system are given to verify the effectiveness of the theoretical results.

Abstract: Collective control of a multiagent system is concerned with designing strategies for a group of autonomous agents operating in a networked environment. The aim is to achieve a global control objective through distributed sensing, communication, computing, and control. It has attracted many researchers from a wide range of disciplines, including the literature of automatic control. This paper aims to give a general framework that is able to accommodate many of these outcomes. Within this framework, the development on this topic is systematically reviewed and the representative outcomes can be sorted out from four aspects: 1) agent dynamics; 2) network topologies; 3) feedback and communication mechanisms; and 4) collective behaviors. Thus, the state-of-the-art approach and technology is described. Moreover, within this framework, further interesting and promising directions on this research topic are envisioned.

Abstract: The paper studies the distributed optimization problem for a class of nonlinear multiagent systems in the presence of external disturbances. To solve the problem, we need to achieve the optimal multiagent consensus based on local cost function information and neighboring information and meanwhile to reject local disturbance signals modeled by an exogenous system. With convex analysis and the internal model approach, we propose a distributed optimization controller for heterogeneous and nonlinear agents in the form of continuous-time minimum-phase systems with unity relative degree. We prove that the proposed design can solve the exact optimization problem with rejecting disturbances.

Abstract: Many cooperative behaviors of multi-agent teams emerge from local interactions among the agents, where an agent interacts with a few “adjacent” teammates, but has no information about the remaining agents. For instance, the selforganization of many biological populations – including swarms of insects, flocks of birds, and schools of fish – are based on such local interaction rules: the motion and decisions of an individual agent are determined by the behavior of its nearest neighbors in the population. A special case of multi-agent coordination is consensus, that is, the agreement of agents on some quantity of interest or, more generally, the full or partial synchronization of their state trajectories. Establishing consensus is a “benchmark” problem in multi-agent systems study, which allows to reveal the main principles of multi-agent coordination and, in particular, the role of the system’s interaction graph (or topology). Consensus lies in the heart of many natural phenomena (e.g., synchronous oscillation of neural cells, which maintains a stable heart rhythm) and engineering designs (e.g., attitude synchronization of satellites). In this article, we give a brief review of distributed consensus algorithms, focusing on the basic ideas and relevant mathematical theory, in particular, graph theoretic methods.

Abstract: Purpose: Decision support systems have become an indispensable tool for managing complex supply chains. This paper develops a multi-agent based supply chain management system that incorporates big data analytics that can exert autonomous corrective control actions. The effects of the system on supply chain agility are explored. Design/methodology/approach: For the development of the architecture of the system, a sequential approach is adopted. First three fundamental dimensions of supply chain agility are identified – responsiveness, flexibility and speed. Then the organisational design of the system is developed. The roles for each of the agents within the framework are defined and the interactions among these agents are modelled. Findings: Applications of the model are discussed, to show how the proposed model can potentially provide enhanced levels in each of the dimensions of supply chain agility. Research limitations/implications: The study shows how the multi-agent systems can assist to overcome the trade-off between supply chain agility and complexity of global supply chains. It also opens up a new research agenda for incorporation of big data and semantic web applications for the design of supply chain information systems. Practical implications: The proposed information system provides integrated capabilities for production, supply chain event and disruption risk management under a collaborative basis. Originality/value. A novel aspect in the design of multi-agent systems is introduced for inter-organisational processes, which incorporates semantic web information and a big data ontology in the agent society.

Abstract: In this paper, we consider the event-triggered distributed average-consensus of discrete-time first-order multiagent systems with limited communication data rate and general directed network topology. In the framework of digital communication network, each agent has a real-valued state but can only exchange finite-bit binary symbolic data sequence with its neighborhood agents at each time step due to the digital communication channels with energy constraints. Novel event-triggered dynamic encoder and decoder for each agent are designed, based on which a distributed control algorithm is proposed. A scheme that selects the number of channel quantization level (number of bits) at each time step is developed, under which all the quantizers in the network are never saturated. The convergence rate of consensus is explicitly characterized, which is related to the scale of network, the maximum degree of nodes, the network structure, the scaling function, the quantization interval, the initial states of agents, the control gain and the event gain. It is also found that under the designed event-triggered protocol, by selecting suitable parameters, for any directed digital network containing a spanning tree, the distributed average consensus can be always achieved with an exponential convergence rate based on merely one bit information exchange between each pair of adjacent agents at each time step. Two simulation examples are provided to illustrate the feasibility of presented protocol and the correctness of the theoretical results.

Abstract: Multiagent systems have been a major area of research for the last 15 years. This interest has been motivated by tasks that can be executed more rapidly in a collaborative manner or that are nearly impossible to carry out otherwise. To be effective, the agents need to have the notion of a common goal shared by the entire network (for instance, a desired formation) and individual control laws to realize the goal. The common goal is typically centralized, in the sense that it involves the state of all the agents at the same time. On the other hand, it is often desirable to have individual control laws that are distributed, in the sense that the desired action of an agent depends only on the measurements and states available at the node and at a small number of neighbors. This is an attractive quality because it implies an overall system that is modular and intrinsically more robust to communication delays and node failures.

Abstract: In 2005, Ren and Beard, under the network connectivity assumption that the union of the directed interaction graphs contains a spanning tree frequently enough, proposed a control protocol to solve the distributed consensus control problem for a multi-agent system with first order integrator agent dynamics. The consensus control protocol for second order systems proposed by the same authors, Ren and Beard, requires that the switching directed interaction graph has a spanning tree at every time instant to guarantee consensus. In this technical note, we propose a distributed consensus control protocol which extends the first order system results of Ren and Beard to agents represented by a general higher order controllable linear system, under the same network connectivity assumption that the union of the directed interaction graphs contains a spanning tree frequently enough. We show that the consensus can be achieved asymptotically by the group of agents under our proposed distributed control protocol. Simulation study illustrates the effectiveness of our proposed method.

Abstract: Steepest descent control laws can be used for formation shape control based on specified inter-agent distances, assuming point agents with single integration of the control action to yield velocity. Separately, it is known how to achieve equal velocity for a collection of agents in a formation using consensus ideas, given appropriate properties for the graph describing information flows. This work shows how the two concepts of formation shape control and flocking behavior can be combined when one changes from an agent with single integration to one with double integration.

Abstract: Since a microgrid consists of various distributed energy resources and local loads, integration of a distributed grid and multiple microgrids leads to an integrated hybrid energy system. This paper focuses on improving profit, economy, security, and dynamic performance of the integrated hybrid energy system by means of developing different areas of management and control strategies upon four-level hierarchical multiagent system. The level 1 agent implements optimal price bidding strategies for high profit of the overall system. The level 2 agent optimizes the energy management strategies for economic operation of each microgrid. The level 3 agent executes coordinated switching control for maintaining security of each microgrid. The level 4 agent facilitates local hybrid control of each distributed energy resource for guaranteeing dynamic performance. Finally, validity of the proposed scheme is tested by means of simulation study.

Abstract: In this paper, a reverse group consensus problem is investigated for the dynamic agents with the inputs in the cooperation-competition network which can be divided into two sub-networks. The weights between the agents in the same sub-network are positive, while the weights between the agents among different sub-networks are negative. Then, the reverse group consensus is firstly studied without the in-degree balance condition. By defining the mirror graph and establishing the solution of the multi-agent system, it is found that the reverse group consensus problem can be achieved if the mirror graph is strongly connected. The explicit expression of the error level is also derived, which would be vanished for multi-agent systems with some special kinds of inputs. Furthermore, as an extension, the decomposing of the cooperation-competition network is discussed, where the concept of the condensation undirected graph and the path balance condition are defined, and several effective sufficient conditions are obtained. Finally, numerical simulation demonstrates the effectiveness of the theoretical analysis.

Abstract: This paper proposes an impulsive framework to analyze the effect of the clustered event-triggered information transmission on the dynamics of multiagent systems. Under this framework, we study three cases and propose different types of event-triggered protocols. First, if agents’ states are available, a clustered event-triggered protocol based on agents’ states is designed, which guarantees all followers to track the leader eventually. Second, the first case is further investigated when the agents’ states are under disturbance. Hence, a modified event-triggered protocol is introduced to reach $\mathcal {L}_\infty$ leader-following consensus. Third, for the case where agents’ states are not available, a clustered event-triggered observer-type protocol is developed based on the agents’ outputs. It is worth noting that Zeno behavior is excluded in these cases. Finally, some numerical examples and an application of unmanned aerial vehicle helicopters are given to verify our theoretical analysis.

Abstract: In this paper, formation control strategies based on position estimation for double-integrator systems are investigated. Firstly, an optimal control formation control strategy is derived based on the estimator. It is proven that the control inputs are able to drive the agents to the predefined formation and the controller is optimal even based on the estimation law if the estimator has converged to stable. Secondly, a consensus law based on the estimator is presented, which enables the agents converge to the formation in a cooperative manner. The stability can be guaranteed by proper parameters. Thirdly, extra control input for inter collision avoidance is added into the derived consensus control strategy, and efficacy analysis are provided in detail. Finally, the effectiveness of the strategies proposed are shown by simulation and experiment results.

Abstract: New Internet of Things IoT applications that leverage ubiquitous connectivity, big data and analytics are enabling Smart City initiatives all over the world. These new applications introduce tremendous new capabilities such as the ability to monitor, manage and control devices remotely, and to create new insights and actionable information from massive streams of real-time data. Supporting this new approach requires the adoption of new paradigms. In this paper, agent tecnology is combined with the emergent concept of Fog computing to design control systems based on the decentralization of control functions over distributed autonomous and cooperative entities that are running at the edge of the network. We describe the Rainbow platform that is designed to bring computation as close as possible to the physical part. Multi-agent systems running on top of Rainbow create smart services using adaptive and decentralized algorithms which exploit the principles of collective intelligence.