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This paper studies the consensus problem of a new multi-agent system via multi-task, multi-control mechanism and multi-consensus strategy. Firstly, two multi-task algorithms are proposed, which can reasonably select some agent nodes with better performance to form the multi-agent system according to different tasks. Secondly, a multi-control protocol designed in this paper integrates continuous control mechanism, impulsive control mechanism and hybrid control mechanism, which has strong applicability and efficiency. Thirdly, a multi-consensus strategy is designed, and it can be converted into "average consensus strategy", "leader-following consensus strategy" and "hybrid consensus strategy" through different parameter settings, which greatly broadens the application field of the multi-agent system. We construct a dynamic model for the proposed multi-agent system, analyze it by using matrix measure and Lyapunov stability theory, and obtain the theorems that enable it to achieve multiple consensus. Finally, the efficiency and practicability of our theories are verified through several simulation experiments. We further carry out some comparative analysis with some relevant papers, and obtain the superiority of our theories in multiple indicators. [ABSTRACT FROM AUTHOR]

• Agents communicate indirectly and asynchronously via a shared cloud repository. • Both globally and locally Lipschitz nonlinearity in system dynamics are discussed. • Adaptive control scheme is considered for multi-agent systems under directed graph. • Practical tracking consensus is achieved for multi-agent systems with disturbances. • Zeno-freeness is guaranteed for the proposed cloud-based self-triggered scheme. This paper addresses the practical tracking consensus problem for first-orderheterogeneous nonlinear multi-agent systems in presence of external disturbances under directed graphs. Agents with limited communication capability interact with each other through a cloud repository, where the information exchange is asynchronous and indirect. The system model is more general than the ones in existing results, moreover, locally Lipschitz nonlinearities and adaptive control schemes are taken into account. It is proven that under the designed self-triggered protocol, agents can track a virtual leader with bounded error and Zeno behaviour is excluded for real implementation. Finally, a numerical simulation demonstrates the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

Almost all systems are inevitably subject to various uncertainties or disturbances from the external environment in practical applications. Taking these factors into consideration, in this paper a distributed algorithm with state noise and gradient disturbance is proposed for solving distributed optimization problem with closed convex set constraint based on multi-agent system under weight-balanced graph. Moreover, based on the gradient tracking and projection methods, the proposed distributed algorithm with gradient tracking can improve the convergence rate by introducing a projection error term and an auxiliary parameter. In contrast to some existing constrained distributed gradient algorithms, the proposed one can make the convergence faster and enhance the performance of convergence. The proposed algorithm is illustrated with two simulation examples to show its effectiveness and robustness. [ABSTRACT FROM AUTHOR]

This paper investigates the optimal consensus problem for heterogeneous discrete-time(DT) linear multi-agent systems. The optimal consensus problem is formulated as finding a global Nash equilibrium solution subjected to the defined local performance index. A reinforcement learning(RL) value iteration(VI) algorithm is introduced to obtain the optimal policies in the sense of Nash equilibrium. To ensure the effectiveness of the VI algorithm, the admissibility of the iterative control policies for multi-agent systems is considered. With theoretical analysis, a new termination criterion is established to guarantee the admissibility of the iterative control policies. Furthermore, an online learning framework is designed with an actor-critic neural network(NN) to implement the VI algorithm. Finally, two simulation examples are presented respectively for leader–follower and leaderless multi-agent systems to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper considers the event-triggered predictive control(ETPC) problem for linear discrete-time multi-agent systems. To achieve accurate state estimation under event-triggered communication between agents, an event-triggered predictive control scheme as well as a novel class of triggering condition are proposed for synchronization of multi-agent systems with single-integrator dynamics, where the control input of each agent no longer remains constant but updates periodically within the time interval between two consecutive triggering instants. Then, the proposed scheme is extended to the general linear multi-agent systems. By comparing with the time-triggered control(TTC) method and the existing event-triggered control(ETC) methods, the control performance of the proposed ETPC scheme is almost the same as that of the TTC approach, and the communication payload between agents is reduced with much less triggering times than existing ETC methods. Finally, two numerical examples are given to verify the theoretical analysis and demonstrate the superiority of the proposed method comparing with some existing methods. [ABSTRACT FROM AUTHOR]

In this paper, a novel control scheme is developed to solve an optimal containment control problem of unknown continuous-time multi-agent systems. Different from traditional adaptive dynamic programming (ADP) algorithms, this paper proposes an internal reinforcement ADP algorithm (IR-ADP), in which the internal reinforcement signals are added in order to facilitate the learning process. Then a distributed containment control law is designed for each agent with the internal reinforcement signal. The convergence of this IR-ADP algorithm and the stability of the closed-loop multi-agent system are analyzed theoretically. For the implementation of the optimal controllers, three neural networks (NNs), namely internal reinforcement NNs, critic NNs and actor NNs, are utilized to approximate the internal reinforcement signals, the performance indices and optimal control laws, respectively. Finally, some simulation results are provided to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, the distributed event-triggered control (ETC) problems for nonlinear stochastic multi-agent systems (MASs) with external disturbance and time-delay are discussed. The leader-following input-to-state actual exponential mean-square consensus (EMSC) criteria are established in networks containing a directed spanning tree. By devising appropriate Lyapunov–Krasovskii function, sufficient conditions for the model to achieve leader-following input-to-state actual EMSC are obtained. The control gain matrix is designed to realize the controller. The lower bounds of interexecution times are given to show that the Zeno-behavior will not appear. Besides, the validity of theoretical analyses is verified by simulation. [ABSTRACT FROM AUTHOR]

The electric vehicle (EV) is gradually being introduced in cities. The impact of this introduction is less due, among other reasons, to the lack of charging infrastructure necessary to satisfy the demand. In today's cities there is no adequate infrastructure and it is necessary to have action plans that allow an easy deployment of a network of EV charging points in current cities. These action plans should try to place the EV charging stations in the most appropriate places for optimizing their use. According to this, this paper presents an agent-oriented approach that analyses the different configurations of possible locations of charging stations for the electric vehicles in a specific city. The proposed multi-agent system takes into account data from a variety of sources such as social networks activity and mobility information in order to estimate the best configurations. The proposed approach employs a genetic algorithm (GA) that tries to optimize the possible configurations of the charging infrastructure. Additionally, a new crossover method for the GA is proposed considering this context. [ABSTRACT FROM AUTHOR]

This paper considers the Internet congestion control and consensus problems of a multi-agent system composed of a congested node, some aggregation nodes and their following nodes. First, a novel multi-agent Internet congestion control system with both discrete and distributed delays is proposed. Second, the stability and Hopf bifurcation of the congested node are discussed. Subsequently, a simple error controller designed for aggregation nodes, some necessary and sufficient conditions have been established to solve the bifurcation consensus problem between aggregating nodes and the congested node. Bifurcation consensus of aggregation nodes and their following nodes are also obtained through consensus protocols which based on a novel predictive controller. So far, the bifurcation asymptotically consensus of multi-agent Internet congestion control system is achieved. It is worth noting that conditions for global stability of system can be obtained by selecting appropriate control parameters, and conclusions of global asymptotic stability consensus is given to draw conditions for avoiding network congestion in the whole system. Finally, numerical examples illustrated the correctness and validity of the main results. [ABSTRACT FROM AUTHOR]

Abstract This paper deals with the model-free optimal containment control problem for a class of linear multi-agent systems (MASs). In the existing results concerning containment control of MASs, the dynamics of the MASs is required to be completely known. Differently, in this paper, we propose a new distributed self-learning control scheme based on action dependent heuristic dynamic programming (ADHDP) to achieve containment control, where the model of MASs is no longer needed. The containment control problem is first transformed into a regulation problem on the dynamics of the designed local containment error. The policy iteration method based on the designed state-action value function (also called the Q -function) is introduced to deal with such a regulation problem. The convergence analysis of this policy iteration method is also given. Neural network (NN) based actor-critic framework is adopted to approximate the optimal Q -functions and the optimal control policies for facilitating the implementation of the proposed method. It shows that the approximated control policies achieve the containment control and satisfy the global Nash equilibrium. Finally, the simulation results are provided to demonstrate the effectiveness of the developed approach. [ABSTRACT FROM AUTHOR]

This paper investigates the fixed-time formation tracking control problem for multi-agent systems with model uncertainties and in absence of leader's velocity measurements. For each follower, a novel fixed-time cascaded leader state observer (FTCLSO) without velocity measurements is first designed to reconstruct the states of the leader. Then, radial basis function neural networks (RBFNNs) are adopted to approximate the model uncertainties online. Based on the proposed FTCLSO and RBFNNs, a novel fixed-time formation control scheme is constructed to address the time-varying formation tracking problem by utilizing fixed-time nonsmooth backstepping technique. The fixed-time convergence of the formation tracking error is guaranteed through Lyapunov stability analysis. Finally, simulation results demonstrate the effectiveness of the proposed formation tracking control scheme. [ABSTRACT FROM AUTHOR]

This paper studies the cooperative attack tolerant tracking control problem for multi-agent systems under sensor attacks. A group of distributed adaptive state observers with a resilient switching scheme is proposed to reconstruct the states, then the tracking control strategy is designed based on the estimates. It is shown that both of the estimation and the attack tolerance control performances are ensured despite of multiple sensor attacks and the disturbances. Finally, the experimental results of networked multi-axis motion control show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a delay tolerant containment control problem for second-order multi-agent systems (MASs) is studied. Considering the disturbance caused by the time-varying delays between agents, we utilize the delay tolerant character of MASs with multiple leaders to redesign the topology of concerned MASs and solve the containment control problem. Corresponding sufficient and necessary conditions on the communication digraph of the MAS are developed to ensure the followers converging to interior points of a convex hull formed by the leaders. Furthermore, considering the inevitable delays between the communications of agents, we present the topology design algorithm in light of the proposed conditions to solve the delay tolerant containment control problem for the second-order MAS. In this case, we focus on how to redesign the communication topology structure instead of constructing a proper distributed cooperative controller. Finally, some examples are given to illustrate the effectiveness of the obtained results. [ABSTRACT FROM AUTHOR]

The containment control problem using only sampled position data for second-order multi-agent systems (MAS) is studied in this paper. We first consider the delay-free case of the containment control protocol and obtain a necessary and sufficient condition regarding the network structure and the system parameters. Then, we obtain a similar necessary and sufficient condition for the case with time delay of the containment control protocol. Finally, several numerical simulations are performed to verify the correctness of the theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, the leader-following and leaderless consensus problem of high-order multi-agent systems on directed graphs are discussed. The delayed sampled outputs are only available data, observer-based output feedback controllers are presented. By constructing appropriate Lyapunov-Krasovskii function and auxiliary integral function to deal with the delayed sampled-data, sufficient conditions are given to ensure the global exponential consensus of the considered systems. We also obtain the upper bounds of the allowable delay and sampling period. Finally, we give the simulation examples to validate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

• An improved multi-agent system based on Fuzzy ARTMAP (FAM) and Q-learning, i.e., IQ-MACS, is proposed. • A trust measurement using a combination of Q-learning and Bayesian formalism is added at team level. • Two real-world case studies, i.e., human motion detection and motor fault detection, with and without noise are used to evaluate the proposed model. • The outcome indicates that IQMACS is able to produce promising results. The Fuzzy ARTMAP (FAM) network is an online supervised neural network that operates by computing the similarity level between the new sample and those prototype nodes stored in its network against a threshold. In our previous study, we have developed a multi-agent system consisting of an ensemble of FAM networks and Q-learning, known as QMACS, for data classification. In this paper, an Improved QMACS (IQMACS) model with trust measurement using a combination of Q-learning and Bayesian formalism is proposed. A number of benchmark and real-world problems, i.e., motor fault detection and human motion detection, are conducted to evaluate the effectiveness of IQMACS. Statistical features are extracted from real-world case studies and utilized for classification with IQMACS, QMACS, and their constituents. The experimental results indicate that IQMACS produces better classification performance by combining the outcomes of its constituents as compared with those of QMACS and other related methods. [ABSTRACT FROM AUTHOR]

In this paper, the consensus for second-order heterogeneous multi-agent system is investigated. The heterogeneous system consists of linear and nonlinear second-order agents, where the nonlinear agents are still with different structures. First, the consensus of the system with and without a static leader is discussed, respectively. Then, we consider a general case where the inputs of linear second-order agents are bounded by virtue of the limitation of the actuators. Effective control protocols to achieve consensus are presented for these cases. Based on graph theory and Lyapunov stability theory, sufficient conditions are established for the consensus of heterogeneous system, where the communication topology is undirected and connected. Finally, several numerical examples are presented to verify the validity of the developed method. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we investigate η (t)-consensus problem of the multi-agent system with directed graph via event-triggered principles. The η (t)-consensus is defined as the difference between agents' states less than a certain preassigned time-dependent bound function η (t). This concept can be regarded as a generalization of the existing concepts such as quasi-consensus/synchronization, where the state differences are requested below a certain constant bound, and exponential synchronization, where the state differences converge to zero exponentially, and more generally, μ -consensus, where the state differences converge to zero with diverse convergence ratios and patterns. The event-triggering principles are asynchronous for each agent and rely on the bound function η (t), as well as its derivative, the agent's own state information and some algebraic information of the network, to determine the next trigger time. We prove that with a loose assumption of the bound function, if the graph is strongly connected graph or generally possesses a spanning tree, then the proposed principles realize η (t)-consensus of the multi-agent system, and moreover, the Zeno behavior is excluded. We give several numerical examples to illustrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

Model-driven development allows IT professionals to specify the system functionality, organization and behavior in a logical or platform-independent manner. Modeling using services allows domain analysts to focus on the added-value and core business the enterprise offers to its stakeholders. Those services are coarse-grained elements able to encapsulate a composition of business process models. The framework presented in this paper provides models together at strategic, tactical and operational levels to develop an agent-oriented software system. The strategic level is concerned with long-term decisions; this top-level uses a service model to understand the business’ high-level (added) values as well as the Quality Expectations and the threats they face. The tactical level is concerned with a broader description of the business processes automated by the system; the i ⁎ strategic dependency and rationale models are used here to further document the service behavior. Actors’ accountability and responsibility can be determined in the visual representation of these strategic and tactical levels. Finally, i ⁎ models are mapped into a set of operational models to document the (multi-agent) system behavior when achieving modeled functionalities. These operational models instantiate the Belief/Desire/Intentions (BDI) paradigm proposing entities – the agents – mapping as closely as possible the real life organization. The paper thus builds a business-driven transformation process leading to a run-time agent-architecture in a single and common framework. It both uses existing models and introduces or refines existing ones to dispose of a method ensuring better alignment and traceability between the business and the IT system. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the distributed resource constrained multi-project scheduling problem (DRCMPSP) and focus on handling resource conflicts. The resource conflicts among projects are originally caused by precedence constraints and resource constraints. To reflect the influences of both constraints, this paper incorporates the critical chain concept into the distributed problem and introduces a corresponding heuristic strategy. Besides, to deal with large scale instances, an elimination mechanism is proposed, which identifies a few conflict time slots in advance and allocates global resources in those time slots only. Depending upon the heuristics and elimination mechanism, we develop a distributed multi-agent system based algorithm called DMAS/EM. Experiments show that DMAS/EM algorithm can generate satisfactory solutions with low average project delay and significant time savings. Furthermore, we observe that our algorithm provides great scalability with respect to variations in the size of instances processed and the degree of conflicts. [ABSTRACT FROM AUTHOR]

In this paper, we study the distributed optimization problem of multi-agent systems with delayed sampled-data, where the interconnected topology is directed, weighted-balanced and strongly connected, and also local cost functions are strongly convex with globally Lipschitz gradients. Based on synchronous and asynchronous sampled-data, we construct two respective algorithms. Our main results, sufficient conditions for the convergence to an optimal solution, are obtained under assumption that all design parameters are chosen properly. We also present one example to validate our theoretical results. [ABSTRACT FROM AUTHOR]

This paper studies the circle formation control for second-order integrator multi-agent systems with event-triggered control strategy. Centralized and decentralized event-triggered control laws are proposed and two corresponding event-trigger conditions are derived respectively using Lyapunov method. With the designed strategies and event-trigger conditions, the second-order system can achieve circle formation control. Finally, numerical simulations are given to illustrate the effectiveness of the proposed event-triggered control strategy. [ABSTRACT FROM AUTHOR]

Event-triggered scheme for multi-agent system is motivated by the network systems with limited communication and energy resources. In our paper, the event-triggered consensus problem of multi-agent system with heterogeneous matching uncertainties is considered. An event-based distributed adaptive controller and a novel event-triggered rule without continuous communication among agents are proposed, with which the consensus errors are uniformly bounded and all the agents could converge to an adjustable small bounded set exponentially. Meanwhile, the Zeno behavior is excluded with the designed control strategy. Finally, numerical examples are given to illustrate the effectiveness of the theoretical analysis. [ABSTRACT FROM AUTHOR]

In this paper, we address a formation control problem with velocity assignment of networked multi-agent systems with heterogeneous time-delays. We assume the situation that the assigned velocity is transmitted as a command signal from human operators via long-range communication. For the cost reduction, only a part of the agents, called leaders, can receive this signal. First, we show that heterogeneous time-delays cause a formation error, and derive the error in an analytic way. Second, we propose a leader selection algorithm to minimize the formation error. Here, a small number of leaders is better from the viewpoint of the cost reduction. To meet this request, we employ the LASSO (least absolute shrinkage and selection operator) which has a function of selecting as few explanatory variables as possible in optimization problems. We focus on this function of the LASSO, and consider applying it to the leader selection problem. The effectiveness of the proposed method is verified by a numerical example. [ABSTRACT FROM AUTHOR]

This paper considers the problem of distributed formation control for a second-order multi-agent system with a directed communication topology and constant communication time delays. A distributed proportion–integration–derivation (DPID) formation control law is proposed for the multi-agent system by introducing the integration information of the formation errors. A delay-independent condition and two delay-dependent conditions on the asymptotical formation stability are respectively presented under the assumption of the communication topology containing a directed spanning tree. Based on the Nyquist stability criterion, the range of the feasible time delays for asymptotical formation stability is obtained by analyzing the relationship between the Nyquist curves and the unit circle. Finally, simulations illustrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, the leaderless consensus problem of multi-agent systems with jointly connected topologies and nonlinear dynamics is considered, in which the nonlinear dynamics are assumed to be non-identical and unknown. The unknown nonlinear dynamics existing in the systems are assumed to be linearly parameterized, and an adaptive design method for leaderless multi-agent systems is presented. By just using the relative position information between each agent and its neighbors, a distributed adaptive consensus control algorithm for the considered systems is proposed, in which the network graphs are jointly connected. Both the global uniform asymptotical stability and the global uniform asymptotical parameter convergence analysis of the adaptive control algorithm are carried out by using adaptive control theory, Lyapunov theory and algebraic graph theory. Finally, an example is given to illustrate the validity of our theoretical results. [ABSTRACT FROM AUTHOR]

This paper is concerned with the consensus problem for continuous time linear multi-agent systems with directed communication topology. An estimation error dynamic is proposed to design the reduced-order observer for dealing with unknown coefficient matrices. To guarantee the consensus, control protocols based on the reduced-order observer are designed to utilize the relative outputs of neighboring agents whose communication topology contains a directed spanning tree. Then the algorithms are extended to the model reference consensus case, where the case of zero control input for reference node is not assumed. Numerical examples are given to illustrate the effectiveness of proposed approaches. [ABSTRACT FROM AUTHOR]

Acquired Brain Injury (ABI) is becoming an epidemic problem in our society, especially among older adults, being known as “the Silent Epidemic”. People with ABI demand solutions in research that offer them with a relearning process such that they can recover not only their physical skills but also their cognitive abilities. In this context, the inherent characteristics of Ambient Intelligence (AmI), transparency and intelligence, have turned it into one of the best approaches to square up to the impairment that ABI can cause. As AmI proposes the development of context-aware systems that integrate different devices to recognize the context and act accordingly, these systems can react promptly to the needs of people with ABI while they carry out their rehabilitation process. Moreover, the exploitation of a Multi-Agent architecture emerges as a natural solution to develop AmI systems, since agents are reactive, proactive and exhibit an intelligent and autonomous behavior. Therefore, in this paper, a Multi-Agent architecture (MAS) for healthcare AmI systems is presented. It contributes to treat people with ABI by using specific devices to control the patient's movements and some physiological responses, such as the variation of the heart rate, during her rehabilitation process. In this way, the natural relationship between AmI and MAS is exploited. Finally, how this system is used to both design and execute therapies for people with ABI is presented. [ABSTRACT FROM AUTHOR]

In line of the research on leader-following consensus, this paper studies the consensus problem for the first-order multi-agent system with a smart leader. Different from previous works where the leader is independent of all the other agents, a kind of leader, called smart leader, is proposed, which can gain and utilize its neighboring followers' position information. Whether such information will be used in adjusting the control algorithm for the smart leader is decided by an event-trigger function. The theory and simulation results show that the proposed model can gain advantage in robustness and fault-tolerance ability with the traditional one. Moreover, a sufficient condition is provided, which can ensure that the position-tracking error and velocity-tracking error are bounded when some actuator faults occur on some of the followers in the framework of the fixed topology. In addition, similar results are also given under switching topology. Finally, some simulation examples are presented for illustration. [ABSTRACT FROM AUTHOR]

Time-varying group formation control problems for second-order multi-agent systems with directed topologies are investigated. Firstly, a time-varying group formation control protocol is constructed using local relative positions and velocities of each agent and its neighbors. Then based on graph theory, nonsingular transformations are applied to the closed-loop multi-agent systems. Sufficient conditions for second-order multi-agent systems to achieve time-varying group formation are further presented together with the time-varying group formation feasibility constraints. Explicit expressions of the subgroup formation reference functions are derived to describe the macroscopic movement of the time-varying subgroup formations. Moreover, by solving an algebraic Riccati equation, an approach to design the time-varying group formation protocol is proposed. Finally, a numerical example with three subgroups is provided to demonstrate the effectiveness of the obtained results. In contrast to the traditional complete formation, where only one formation is realized by the multi-agent system, in the group formation discussed in the current paper, agents are classified into subgroups and each subgroup is required to form a specified time-varying sub-formation via inter-subgroup and intra-subgroup interactions. [ABSTRACT FROM AUTHOR]

In this paper, the recent developments of distributed coordination control problems are summarized in a graph-theory-based framework. Distributed coordination has attracted tremendous attention in control and robot communities because of its potential applications in the past decade. The graph is used to describe the interconnections among agents, and different distributed coordination control problems, such as consensus, formation control, rendezvous, alignment, swarming, flocking, containment control and circumnavigation control, are adopted to this description by considering different cooperative objects. Therefore it is natural to study the distributed coordination control problems via graph theory, and the graph-theory-based results on consensus, formation control, and some closely related issues, i.e., rendezvous/alignment, swarming/flocking, containment control and circumnavigation control, are reviewed, and provide a cohesive overview in the coordination control problems, in system modeling, control law designs and analysis, and structure transformation. Finally, towards the practical applications, some potential directions possibly deserving investigation in distributed coordination control are discussed. [ABSTRACT FROM AUTHOR]

Abstract: This paper deals with optimizing the distribution of services performed by agents in a mobile agent network (MAN) using a genetic algorithm. The MAN is a model which consists of agents and various network elements and nodes. Each agent is capable of performing remote software management services. In order to verify the efficiency of various service distributions on different network topologies, we developed a mobile agent network simulator. This simulator allows the user to define the services which need to be performed by agents and describe the physical network used in the process. In this paper, an approach using a genetic algorithm is proposed. This algorithm is used to generate a suboptimal distribution of services among agents which optimizes the total execution time of services on a variety of network topologies with different network parameters. [Copyright &y& Elsevier]

This paper investigates the consensus of second-order multi-agent systems without measuring the velocity states of the agents, where each agent can be either a double integrator or a harmonic oscillator. By utilizing the position information of the agents, a distributed observer-based protocol is proposed to solve the second-order consensus problem of multi-agent systems with or without time delay. The observer-based consensus problem is converted to the stability analysis for a set of second-order quasi-polynomials through coordinate transform and system decomposition. Then, some necessary and sufficient conditions are derived for reaching second-order consensus in multi-agent systems with or without time delay, respectively, and it is shown that the consensus can be achieved if and only if the communication delay is less than a critical value. Simulation examples are given to verify the theoretical analysis. [ABSTRACT FROM AUTHOR]

In this paper, consensus problem of general nonlinear multi-agent systems with communication failures is studied based on a distributed consensus algorithm. Under the assumption that the communication graphs are strongly connected, we derive some sufficient consensus criterion for both cases fixed and switched topologies using the relative measured output states of each agent. Moreover, the case that there is a leader in the communication graph is also explicitly investigated. Finally, simulation examples are presented to show the effectiveness of the obtained results. [ABSTRACT FROM AUTHOR]

This paper considers the leader-following tracking problem of second-order nonlinear multi-agent systems with a reference leader. It is assumed that all following agents can only access the relative position information with its neighbors, the position and velocity information of the leader is only accessed by a subset of the following agents, and the leader׳s bounded reference input cannot be available by any following agents. To track the active leader, a distributed discontinuous observer-based consensus protocol is proposed for each following agent, whose observer is used to estimate the agent׳s unmeasured velocity. Under connected undirected interaction topology, the proposed protocol can be used to solve the second-order nonlinear multi-agent consensus problem. While the reference input of the leader is known, the proposed protocol can solve the consensus problem under a class of directed switching interaction topologies. Furthermore, to make the designed approach in fully distributed fashion, the distributed adaptive laws are provided to design the protocol coupling parameters. Finally, a numerical example is presented to illustrate our obtained result. [ABSTRACT FROM AUTHOR]

Abstract: This paper investigates the problem of distributed output feedback consensus for multi-agent systems. Both fixed topology and stochastic switching topology are considered. It is assumed that each agent updates its state according to the output information of itself and its neighbors. We obtain some necessary and sufficient conditions of consensusability based on algebraic graph theory and Markov jump linear system theory. Algorithms are given to derive the allowable control gains. Simulation results are presented to show the effectiveness of the theoretical results. [Copyright &y& Elsevier]

Abstract: Situation awareness is a computing paradigm which allows applications to sense parameters in the environment, comprehend their meaning and project their status in the next future. In collaborative situation awareness, a challenging area in the field of Ambient Intelligence applications, situation patterns emerge from users' collective behavior. In this paper we introduce a multi-agent system that exploits positioning information coming from mobile devices to detect the occurrence of user's situations related to social events. In the functional view of the system, the first level of information processing is managed by marking agents which leave marks in the environment in correspondence to the users' positions. The accumulation of marks enables a stigmergic cooperation mechanism, generating short-term memory structures in the local environment. Information provided by such structures is granulated by event agents which associate a certainty degree with each event. Finally, an inference level, managed by situation agents, deduces user situations from the underlying events by exploiting fuzzy rules whose parameters are generated automatically by a neuro-fuzzy approach. Fuzziness allows the system to cope with the uncertainty of the events. In the architectural view of the system, we adopt semantic web standards to guarantee structural interoperability in an open application environment. The system has been tested on different real-world scenarios to show the effectiveness of the proposed approach. [Copyright &y& Elsevier]

Abstract: This paper investigates the leader-following consensus problem of discrete-time multi-agent systems. The dynamics of the leader and all following agents adopt the same general form of a linear model that can be of any order. The interconnection topology among the agents is assumed to be switching and undirected. To track the active leader, two kinds of distributed observer-based consensus protocols are proposed for each following agent, whose distributed observers are used to estimate the leader's state and the tracking error based on the relative outputs of neighboring agents, respectively. In light of the modified discrete-time algebraic Riccati equality and Lyapunov method, we prove that the discrete-time leader-following consensus problem can be solved by proposing the distributed observer-based consensus protocol under switching topologies. Finally, a numerical example is given to illustrate the obtained result. [Copyright &y& Elsevier]

Abstract: This paper investigates a flocking problem of multiple agents with heterogeneous nonlinear dynamics. In order to avoid fragmentation, we construct a potential function and a connectivity-preserving flocking algorithm to enable the multiple agents to move with the same velocity while preserving the connectivity of underlying networks with a mild assumption that the initial network is connected and the coupling strength of the initial network of the nonlinear velocity consensus term is larger than a threshold value. Furthermore the proposed flocking algorithm is extended to solve the problem of multi-agent systems with a nonlinear dynamical virtual leader. The result is that all agents' velocities asymptotically approach to the velocity of the virtual leader, and the distance between any two agents is asymptotically stabilized to avoid collisions among agents. Finally, some numerical simulations are presented to illustrate the effectiveness of the theoretical results. [Copyright &y& Elsevier]