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This paper focuses on studying the optimization problem of multi-agent systems (MAS) under undirected graph. To reduce the communication frequency among agents, a zero-gradient-sum (ZGS) algorithm based on dynamic event-triggered (DET) mechanism is investigated. The event-triggered condition of each agent only uses its own state information and the neighbor's state information at the previous triggering instants, without requiring continuous state information from the neighbor. In addition, the designed algorithm allows for the sampling period to be arbitrarily large. The Lyapunov method is utilized to derive the sufficient conditions that incorporate time delay and parameters. As the event is only checked at the periodic moment, zeno behavior can be directly excluded. Finally, numerical simulations demonstrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

This paper designs event-triggered delay compensation and disturbance attenuation control algorithms for multi-agent systems. First, through predictor-based model reduction method, an unknown large input delay can be compensated and delay-free dynamics is obtained. Based on the transformed systems, predictive descriptor observers are proposed to obtain estimated system states and sensor faults. Then, an event-triggered fault-tolerant algorithm is designed to save energy resources for communication among the agents while attenuating the disturbances. Moreover, it is validated that the systems do not exhibit Zeno behaviour using the designed event-triggering sequence. Numerical simulations are conducted to demonstrate the effectiveness of designed algorithms. [ABSTRACT FROM AUTHOR]

This paper studies the problem of distributed predefined-time optimization for leaderless consensus of second-order multi-agent systems under a class of weighted digraphs. The proposed framework has two main steps. In the first step, the agents communicate to perform a consensus-based distributed predefined-time optimization and to generate a constant optimal output reference for each agent. In the second step, each agent tracks its corresponding optimal output reference, using a sliding-mode controller to reach the global optimum in a predefined time, even under matched disturbances. The proposed algorithm relies explicitly on user-defined constant parameters. Numerical simulations are performed to validate the efficacy of the algorithm. [ABSTRACT FROM AUTHOR]

This paper investigates the data-driven consensus tracking problem for heterogeneous multi-agent systems. All of the heterogeneous dynamics, disturbances and measurement noises are considered for the output tracking consensus of nonlinear multi-agent network under a data-driven scheme. A dynamic linearisation method is introduced to deal with the nonlinear nonaffine structures of the agents and a heterogeneous linear data model for the agent is obtained due to the heterogeneous dynamics of the agent itself. On this basis, a distributed model-free adaptive predictive control (DMFAPC) algorithm is constructed by the use of consensus errors and the robustness analysis is conducted in the presence of disturbances. Further, the results are modified by using measured outputs to replace the system outputs, and thus a measured output-based DMFAPC is presented to deal with the omnipresent measurement noises in practical applications. The two proposed DMFAPC methods are data driven since no mechanistic model information is required wherein. And thus, no unmodelled dynamics affects the consensus performance. Instead, they can improve the control performance by utilising additional predictive information. The two proposed DMFAPC methods are verified using numerical simulations. [ABSTRACT FROM AUTHOR]

This paper is concerned with the distributed optimisation problem over a multi-agent network, where the objective function is described by a sum of all the local objectives of agents. The target of agents is to collectively reach an optimal solution while minimising the global objective function. Under the assumption that the information exchange among agents is depicted by a sequence of time-varying undirected graphs, a distributed optimisation algorithm with uncoordinated time-varying step-sizes is presented, which signifies that the step-sizes of agents are not always uniform per iteration. In light of some reasonable assumptions, this paper fully conducts an explicit analysis for the convergence rate of the optimisation method. A striking feature is that the algorithm has a geometric convergence rate even if the step-sizes are time-varying and uncoordinated. Simulation results on two numerical experiments in power systems show effectiveness and performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper considers the distributed event-triggered consensus problem for multi-agent systems with general linear dynamics under a directed graph. We propose a novel distributed event-triggered consensus controller with state-dependent threshold for each agent to achieve consensus. In this strategy, continuous communication in both controller update and triggering condition monitoring is not required, which means the proposed strategy is fully continuous communication free. Each agent only needs to monitor its own state continuously to determine if the event is triggered. Additionally, the approach shown here provides consensus with guaranteed positive inter-event time intervals. Therefore, there is no Zeno behavior under the proposed consensus control algorithm. Finally, numerical simulations are given to illustrate the theoretical results. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, the distributed convex optimisation problem of the multi-agent system over an undirected network is investigated, in which the local objective function of each agent is only known by itself. To reduce the communication consumption between agents, a state-based dynamic event-triggered algorithm with positive minimum inter-event time (MIET) is provided, where the aperiodic information communication only occurs at some discrete triggering time instants. Moreover, the sampling control technology is combined into the previous event-triggered algorithm for verifying the event-triggered condition at every sampling time, instead of continuous access. Finally, several numerical simulations are presented for illustrating and verifying the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, the mechanism of the human–computer game is investigated with the help of multi-agent systems (MASs) and reinforcement learning (RL). The game is formulated as a bipartite consensus problem while the interactions among humans and computers are modelled as a multi-agent system over a coopetition network. The coopetition network associated with the multi-agent system is represented by a signed graph, where positive/negative edges denote cooperative/competitive interactions. We assume the decision mechanism of the agents are model free and each agent has to make a distributed decision by learning the input and output data from himself/itself and his/its neighbours. The individual decision is developed with the neighbours' state information and a performance index function. A policy iteration (PI) algorithm is proposed to solve the Hamilton-Jacobi-Bellman equation and obtain the optimal decision strategy. Furthermore, an actor-critic neural network is adopted to approximate the performance index and the optimal decision strategy in an online manner. The simulation results are finally given to validate the proposed reinforcement learning approach. [ABSTRACT FROM AUTHOR]

For an uncontrollable system, adding leaders and adjusting edge weights are two methods to improve controllability. In this paper, the controllability of multi-agent systems under directed topologies is studied, especially on the leader selection problem and the weight adjustment problem. For a multi-agent system, necessary and sufficient algebraic conditions for controllability with fewest leaders are proposed. To improve controllability by adjusting edge weights, the system is supposed to be structurally controllable, which holds if and only if the communication topology contains a spanning tree. It is also proved that the number of the fewest edges needed to be assigned on new weights equals the rank deficiency of controllability matrix. In addition, a leader selection algorithm and a weight adjustment algorithm are presented. Simulation examples are provided to illustrate the theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, an energy-based control methodology is proposed to satisfy the Reynolds three rules in a flock of multiple agents. First, a control law is provided that is directly derived from the passivity theorem. In the next step, the Number of Neighbours Alignment/Repulsion algorithm is introduced for a flock of agents which loses the cohesion ability and uniformly joint connectivity condition. With this method, each agent tries to follow the agents which escape its neighbourhood by considering the velocity of escape time and number of neighbours. It is mathematically proved that the motion of multiple agents converges to a rigid and uncrowded flock if the group is jointly connected just for an instant. Moreover, the conditions for collision avoidance are guaranteed during the entire process. Finally, simulation results are presented to show the effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

A continuous-time distributed algorithm based on multi-agent systems is proposed to solve the convex nonsmooth optimization with globally coupled constraints in this paper. Firstly, the relationship of equivalence between the equilibrium and the optimal solution of the system is proved by using the properties of projection operator and saddle point. In addition, the stability of the algorithm is analyzed by means of Lie derivative and set-valued LaSalle's invariance principle. In particular, the trajectory of the proposed algorithm from any initial point can converge to the global optimal solution. Finally, the effectiveness of the designed algorithm is verified by numerical simulations. [ABSTRACT FROM AUTHOR]

This paper investigates the prescribed-time distributed Nash equilibrium seeking (DNES) problem for multi-agent noncooperation games. Based on the distributed motion-planning method and the gradient search, a class of prescribed-time DNES algorithms are developed for first and second-order multi-agent systems, respectively. They may ensure each player' s action converges to the Nash equilibrium (NE) point at a prescribed settling time in advance. Further, for the case when the velocity information of each agent is not available, an observer-based prescribed-time DNES algorithm is extended. Compared with existing works, the convergence time of proposed prescribed-time DNES algorithms in this paper can be assigned in advance by users according to task requirements. Besides, the designed DNES algorithms are sampled without continuous measurements, which means players can update their actions at each sampling moment to avoid the increasing cost brought by continuous information interaction among players. Finally, the effectiveness of algorithms proposed in this paper is verified by some numerical simulations. [ABSTRACT FROM AUTHOR]

This paper focuses on the resource allocation problem(RAP) with constraints under a fixed general directed topology by using the distributed sub-gradient algorithm with event-triggered scheme in multi-agent systems, where each agent owns a cost function and its state value is bounded. The distributed sub-gradient algorithm aims to minimise the total cost by a distributed manner while achieving an optimal solution. Unlike centralised methods, the triggering condition and algorithm for each agent are fully decentralised. At each instant of time, each agent updates its state by employing the states which are collected from itself and its neighbouring agents at their last triggering time. In order to illustrate the effectiveness of the proposed sub-gradient algorithm with event-triggered control law, one simulation example is presented before the conclusion. [ABSTRACT FROM AUTHOR]

This paper studies the differential privacy-preserving problem for multi-agent systems (MASs) in the presence of antagonistic information over signed digraph. As for the structurally balanced case, an ε -differential privacy algorithm is proposed, upon which some sufficient conditions guaranteeing almost sure bipartite consensus are given. Based on the above scheme, the tradeoff between the system performance and the privacy guarantee is elaborated, and the optimal noise is also devised. Moreover, the proposed privacy preserving scheme is further applied to the scenario of structurally unbalanced graph, where a criterion with respect to almost sure stability of the considered system is derived, as well as the privacy preserving condition. This extends the balanced interaction scenario, and consequently the cooperative multi-agent systems. Finally, numerical simulations are presented to demonstrate the effectiveness of our results. [ABSTRACT FROM AUTHOR]

This article discusses the issue of robust adaptive neural network (NN) consensus tracking control for nonlinear strict-feedback multi-agent systems with quantised input. By combining the neural network approach with robust techniques, a novel switching function is introduced to guarantee the tracking performance of this system. To estimate the unmeasured state, an NN-based adaptive state observer is developed. Based on backstepping dynamic surface control algorithms, a robust output feedback controller is constructed to guarantee that all signals in the closed-loop system remain globally uniformly ultimately bounded. Finally, numerical simulations are carried out to demonstrate the effectiveness of the presented algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a randomized parallel algorithm is proposed to solve the distributed optimal consensus problem of multi-agent systems. Involving both the transient response and the final consensus state, the problem is described as a constrained non-separable optimization problem. Inspired by the randomized Jacobi proximal alternating direction method of multipliers, the proposed algorithm makes it possible for only a fraction of agents to solve their private subproblems in parallel at each iteration, which greatly saves computational resources and enhances running efficiency. The convergence analysis of the algorithm gives fully distributed convergence conditions. A trade-off between the convergence speed and resource savings is then obtained, where the convergence rate is estimated to be at least O 1 t . Furthermore, the algorithm can be accelerated to enjoy a convergence rate of O 1 t 2 by adaptively adjusting the auxiliary parameters properly. Numerical simulations demonstrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

Natural or man-made disasters can cause different kinds of moving obstacles (e.g., fires, plumes, floods), which make some parts of the road network temporarily unavailable. After such incidents occur, responders have to go to different destinations to perform their tasks in the environment affected by the disaster. Therefore they need a path planner that is capable of dealing with such moving obstacles, as well as generating and coordinating their routes quickly and efficiently. In this paper, we present a novel approach for using a multi-agent system for navigating one or multiple responders to one or multiple destinations in the presence of moving obstacles. Our navigation system supports information collection from hazard simulations, spatio-temporal data processing and analysis, connection with a geo-database, and route generation in dynamic environments affected by disasters. We design and develop a set of software geospatial agents that assist emergency actors in dealing with the spatio-temporal data required for emergency navigation, based on their roles in the disaster response. One of the key components of the system is the path planning module, which combines the modified A* algorithm, insertion heuristics, and auction algorithm to calculate obstacle-avoiding routes for multiple responders with multiple destinations. A spatial data model is designed to support the storage of information about the tasks and routes produced during the disaster response. Our system has been validated using four navigation cases. Some preliminary results are presented in this paper and show the potential of the system for solving more navigation cases. [ABSTRACT FROM AUTHOR]

In order to solve the problem that communication and computing resources are consumed in the first order multiagent system circle formation with communication delay, this paper introduced an event-driven control mechanism, and designed the control law for arbitrary circle formation, which was coupling with state dependent and state independent event-driven conditions. Event-triggered function was based on state error, made the information transmission between two neighbor agents and the controller updates driven by properly defined events. It proved the convergence of the control law strictly and theoretically, and the numerical simulation validated the effectiveness of control algorithm. The simulation results also s how that the updating frequency of the controller and the resource consumption of the agent are reduced with the desired system performance. [ABSTRACT FROM AUTHOR]

Abstract This paper investigates the adaptive consensus problem of multi-agent nonlinear systems with odd impulsive sequence. To reduce the required information transmitted among the nodes, the distributed impulsive control protocol is designed. Considering the unknown system parameters in real system, the adaptive control technique is also used and some novel sufficient consensus conditions are derived to achieve the consensus of multi-agent nonlinear systems. The proposed odd impulsive control can obtain larger control region than the common impulsive consensus method with fixed impulsive instants, which is more flexible and reliable in practical application. Finally, two simulations are given to illustrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, distance-based formation control laws for the multi-agent systems are proposed when the reference velocity is unknown to the followers. Especially, not only the shape but the orientation of formation is also controlled. Each agent measures only the relative positions of neighbors with respect to their own local coordinate system. The local coordinate systems in each agent are not aligned and unknown to the other agents; thus the control laws are completely decentralized. Using the measured local information, the unknown reference velocity is estimated by applying an adaptive method. The shape and orientation of formation are controlled based on the estimated reference velocity. The stability and convergence of the system are analyzed mathematically and verified through numerical simulation. [ABSTRACT FROM AUTHOR]

This paper describes the implementation of the fully traffic adaptive signal control algorithm by Lämmer 2 in the agent-based transport simulation MATSim. The implementation is tested at an illustrative, single intersection scenario and compared to the results of Lammers MATLAB simulation. Plausibility of the self-controlled signals and overall results can be confirmed. Small deviations can be explained by differences in flow simulation and resolution of simulation time steps. In the simulation of the illustrative intersection, the adaptive control is proved to be stable and, overall, superior to a fixed-time control. Constant vehicle arrivals are simulated to show the performance of the control and its underlying sub-strategies. The expected behavior of the algorithm and its implementation are validated by analyzing queue lengths over time. The adaptive control significantly outperforms the fixed-time control for stochastic demand, where its ability to dynamically react to changes in flow becomes important. [ABSTRACT FROM AUTHOR]

This paper investigates the mean square consensus problem of dynamical networks of leader-following multi-agent systems with measurement noises and time-varying delays. We consider that the fixed undirected communication topologies are connected. A neighbor-based tracking algorithm together with distributed estimators are presented. Using tools of algebraic graph theory and the Gronwall-Bellman-Halanay type inequality, we establish sufficient conditions to reach consensus in mean square sense via the proposed consensus protocols. Finally, a numerical simulation is provided to demonstrate the effectiveness of the obtained theoretical result. [ABSTRACT FROM AUTHOR]

This paper investigates the distributed shortest-distance problem of multiagent systems where agents satisfy the same continuous-time dynamics. The objective of multiagent systems is to find a common point for all agents to minimize the sum of the distances from each agent to its corresponding convex region. A distributed consensus algorithm is proposed based on local information. A sufficient condition also is given to guarantee the consensus. The simulation example shows that the distributed shortest-distance consensus algorithm is effective for our theoretical results. [ABSTRACT FROM AUTHOR]

This paper studies a distributed state estimation problem for a network of linear dynamic systems (called nodes), which evolve autonomously, but their measurements are coupled through neighborhood interactions. Power networks are typical networked systems obeying such features, with other examples including traffic networks, sensor networks and many multi-agent systems. We develop a new distributed state estimation approach, for each node to update its local state. The core of this distributed approach is a distributed maximum a posteriori (MAP) estimation technique, which delivers a globally optimal estimate under certain assumptions. We apply the distributed approach to an IEEE 118-bus system, and compare it with a centralized approach, which provides the optimal state estimate using all the measurements, and with a local state estimation approach, which uses only local measurements to estimate local states. Simulation results show that under different scenarios including normal operation, bad measurements and sudden load change, the distributed approach is clearly more accurate than the local state estimation approach and distributed static state estimation approach. Although the result is a bit less accurate than that by a centralized algorithm, the distributed algorithm enjoys low computational complexity and communication load, and is scalable to large power networks. [ABSTRACT FROM AUTHOR]

Time-varying formation analysis and design problems for multi-agent systems with general linear dynamics and switching directed interaction topologies are investigated. Different from the previous results, the formation in this paper can be defined by specified piecewise continuously differentiable vectors and the switching topologies are directed. Firstly, necessary and sufficient conditions for general linear multi-agent systems with switching directed topologies to achieve time-varying formations are proposed, where a description of the feasible time-varying formation set and approaches to expand the feasible formation set are given. Then an explicit expression of the time-varying formation reference function is derived to describe the macroscopic movement of the whole formation. An approach to assign the motion modes of the formation reference is provided. Moreover, an algorithm consisting of four steps to design the formation protocol is presented. In the case where the given time-varying formation belongs to the feasible formation set, it is proven that by designing the formation protocol using the proposed algorithm, time-varying formation can be achieved by multi-agent systems with general linear dynamics and switching directed topologies if the dwell time is larger than a positive threshold. Finally, numerical simulations are presented to demonstrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

This paper proposes a novel distributed event-triggered algorithmic solution to the multi-agent average consensus problem for networks whose communication topology is described by weight-balanced, strongly connected digraphs. The proposed event-triggered communication and control strategy does not rely on individual agents having continuous or periodic access to information about the state of their neighbors. In addition, it does not require the agents to have a priori knowledge of any global parameter to execute the algorithm. We show that, under the proposed law, events cannot be triggered an infinite number of times in any finite period (i.e., no Zeno behavior), and that the resulting network executions provably converge to the average of the initial agents’ states exponentially fast. We also provide weaker conditions on connectivity under which convergence is guaranteed when the communication topology is switching. Finally, we also propose and analyze a periodic implementation of our algorithm where the relevant triggering functions do not need to be evaluated continuously. Simulations illustrate our results and provide comparisons with other existing algorithms. [ABSTRACT FROM AUTHOR]

This paper studies the coordination of nonlinear multi-agent systems with multiple leaders and stochastic disturbance. Two impulsive control algorithms are proposed to make all agents to track the convex set determined by the multiple leaders under both fixed and switching topologies. Based on Lyapunov function and comparison principle of impulsive systems, some results on the convergence of the controlled systems are obtained. Finally, some numerical simulations demonstrate the validity of the proposed control algorithms. [ABSTRACT FROM AUTHOR]

This paper studies the problem of distributed containment control for multi-agent systems with high-order dynamics and input delays. Two event-triggered control algorithms are proposed for multi-agent systems without and with input delay, respectively. The communication instants between two linked followers are determined by the event-triggering condition, and every follower can detect the event based on its own control input. For the followers, edge-based estimators are adopted to predict state differences to neighbors. Control inputs of the followers are calculated based on the predicted values of the state differences. To deal with the input delay, a delay comprehension approach is developed. It is proved that for arbitrarily large but bounded input delays, the followers can move into the convex hull spanned by the leaders asymptotically. Simulation results show the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we consider fast convergent average consensus based on community detection algorithm. Generally, we know that a small network can have a faster convergence speed than a big one at the same condition. So we divide a multiagent network into several small networks. Firstly, let every small network reach own consensus, and then entire network reach the average consensus. Based on this idea, we present FCWAC Algorithm. For the FCWAC Algorithm, we obtain the results on the average consensus of first-, second-, and high-order continuous-time multiagent systems. Finally, simulation examples illustrate our theoretical results. [ABSTRACT FROM AUTHOR]

Multi-agent technology is applied in this paper to solve the distribution system service restoration problems. Specifically, switches in the distribution systems are modeled as intelligent agents and organized as local power committees in a distribution system. The advantage of this approach is to utilize the solving ability of a local committee of agents who could coordinate with neighboring committees to achieve an agreement that satisfies the global objective of the power distribution system service restoration problems without relying on a pre-determined centralized optimization algorithm as traditional approaches. The proposed coordination method helps switches in the distributed local power committees to reach a consensus among many proposed service restoration solutions after faults are detected and isolated. We also take the voltage violations into consideration in the search of solutions. Two examples of different power distribution systems are presented to demonstrate how the algorithms of the coordination method can identify the best restoration solution among many proposed local candidate solutions. The simulation results show that the proposed committee-based multi-agent approach yields better performance than the previous methods. [ABSTRACT FROM AUTHOR]

This paper presents a new circumnavigation pattern — safe convex-circumnavigation, for the agent to enclose the targets closely, which is a pattern with better performance in harsh environments than the traditional circular circumnavigation pattern. With this new pattern, the agent circumnavigates the convex hull of multiple stationary targets with an expected enclosing distance and speed using the agent's known position as well as the bearing-only information of the targets, in addition, no collision with this convex hull happens in the whole time domain. An algorithm containing scalar estimators and a control protocol is proposed to implement this pattern. The estimators are designed to locate the targets. In the control protocol, the velocity of the agent is decomposed to one directly pointing to the convex hull and one orthogonal to its current direction. The agent is forced to circumnavigate the convex hull of the targets' estimated positions and finally circumnavigate the convex hull of the real targets because the estimators converge to the exact positions of the targets. The corresponding convergence analysis is provided subsequently. Numerical simulation and real time experiment are conducted to verify the proposed method. [ABSTRACT FROM AUTHOR]

This paper focuses on the distributed consensus problem for multiagent systems subject to high-order integrator dynamics and unknown control and output directions based on switching topologies. Unlike existing works, both control and output directions are unknown in the systems and delay is considered between the switching of candidate K-filters and that of topologies. In these conditions, we produced a fresh distributed consensus algorithm by the correlative output information. By designing asynchronous switched K-filters and classical K-filters, the state variables of the leader and all followers are reconstructed. Based on the backstepping method and the Nussbaum-type functions, we put forward the distributed controller with K-filters for the follower. Then, we scrupulously prove that all agents can acquire consensus driven by the controller if the switching signal satisfies an average dwell time condition. Finally, the correctness of the algorithm is tested based on a numerical simulation. [ABSTRACT FROM AUTHOR]

This paper studies an event-triggered distributed optimal coordination problem for heterogeneous linear multi-agent systems with external disturbances. A new distributed optimal coordination algorithm based on event-triggered communication is proposed to deal with heterogeneous agents having a relative degree two. By utilizing the input-to-state stability guaranteed by the proposed algorithm, a novel distributed event-triggering condition is designed. It is showed that the robust distributed optimal coordination problem can be solved practically without Zeno behavior. Moreover, the relationship between the parameters in event-triggering conditions and the system performance is also studied. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

To deal with the consensus problem of general first-order multi-agent systems with intermittent communication, a persistent-hold consensus algorithm is designed by introducing the hold control after the usual intermittent control. Under the fixed topology, consensus convergence conditions are obtained based on matrix theory and graph theory for the agents under our proposed persistent-hold strategy, and the results show that our proposed strategy has less conservative converging conditions than the usual intermittent control. Besides, consensus convergence conditions are also obtained for the agents under the switching topologies. Numerical simulations show the correctness of theoretical results. [ABSTRACT FROM AUTHOR]

Consensus problems for both leaderless and leader-follower disturbed higher-order multi-agent systems are investigated in this study. Based on the combination of integral sliding-mode control and disturbance observer techniques, composite sliding-mode consensus protocols are proposed for leaderless and leader-follower disturbed multi-agent systems, respectively. First, for the leaderless case, composite sliding-mode consensus protocols are designed to make the agents achieve asymptotic consensus. Second, for the leader-follower case, finite-time composite sliding-mode consensus protocols are presented to make the followers achieve consensus with the leader in finite time. Then based on the consensus algorithms proposed in this study, a consensus control strategy is given to realise the velocity and altitude finite-time consensus for a class of leader-follower multi-hypersonic vehicle systems. Finally, the numerical simulations are conducted to validate the efficiency of the proposed consensus algorithms. [ABSTRACT FROM AUTHOR]

In this study, the authors propose a new algorithm for the flocking problem of multi-agent systems with the dynamic virtual leader under the sampled-data frameworks. A necessary time-varying condition is derived on the sampling period for preserving connectivity of the network and avoiding collision among agents. Then, defining a new energy function, it is proved that using a connectivity-preserving potential function and under the sampling period which satisfies the necessary condition, an asymptotic flocking motion is achievable. Under the proposed sampled-data flocking algorithm, the connectivity preservation of the network and collision avoidance among agents are guaranteed as well as the velocity convergence of all agents to that of the virtual leader is ensured. Moreover, employing the upper bound of the energy function, they specify an upper bound of the sampling period which could satisfy the time-varying necessary condition. Finally, they present a numerical simulation to illustrate the theoretical results. [ABSTRACT FROM AUTHOR]

We consider a team of heterogeneous robots, which are deployed within a common workspace to gather different types of data. The robots have different roles due to different capabilities: some gather data from the workspace (source robots) and others receive data from source robots and upload them to a data center (relay robots). The data-gathering tasks are specified locally to each source robot as high-level linear temporal logic formulas, which capture the different types of data that need to be gathered at different regions of interest. All robots have a limited buffer to store the data. Thus, the data gathered by source robots should be transferred to relay robots before their buffers overflow, respecting at the same time a limited communication range for all robots. The main contribution of this work is a distributed motion coordination and intermittent communication scheme that guarantees the satisfaction of all local tasks, while obeying the above constraints. The robot motion and interrobot communication are closely coupled and coordinated during runtime by scheduling intermittent meeting events to facilitate the local plan execution. We present both numerical simulations and experimental studies to demonstrate the advantages of the proposed method over existing approaches that predominantly require all-time network connectivity. [ABSTRACT FROM AUTHOR]

A class of persistent monitoring problems in one-dimensional space with the aid of multi-agent systems, which is formulated as an optimisation problem of minimising a novel performance metric is studied. The concept of the monitoring index is introduced in the performance metric to describe the importance of different targets in the space due to the requirements of the monitoring task. Based on a given class of moving trajectories, an optimal control approach is used to minimise the performance metric of monitoring space. After some model transformation, the infinitesimal perturbation analysis method is used to design the gradient-based algorithm, aiming to find the optimal trajectories, where the monitoring index exhibits the effect on the monitoring time for different targets. Numerical simulations are represented to demonstrate the performance of the algorithm and reveal the effectiveness of the monitoring index. [ABSTRACT FROM AUTHOR]

A robust consensus controller is proposed for heterogeneous higher-order nonlinear multi-agent systems, when the agent dynamics are involved with mismatched uncertainties. A distributed consensus protocol based on a time-varying nonhomogeneous finite-time disturbance observer and sliding mode control is designed to realize the network consensus of higher-order multi-agent systems. The time-varying finite-time disturbance observer overcomes the problem of peaking value near the initial time caused by the constant gain one and is designed to estimate the uncertainties and to mitigate the effect of mismatched uncertainties during the sliding mode. To eliminate the chattering phenomenon and ensure finite-time convergence to the sliding surface, the control law is designed by using the super twisting algorithm. Finally numerical simulations are given to illustrate the validity of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The event-based broadcasting containment control problem for both first-order and second-order multi-agent systems under directed topology is investigated. Based on certain event, each agent decides when to transmit its current states to its neighbours and the agents’ distributed control algorithms are based on these sampled state measurements, which can significantly decrease the number of the controllers’ updates. All the agents are divided into two groups, namely, the leaders and the followers. The formation control is introduced. The leaders exchange their information to converge to a formation. The followers utilise the information from both their leader neighbours and their follower neighbours and are driven to the convex hull of the leaders using the proposed control algorithms. Numerical simulations are provided to illustrate the effectiveness of the obtained theoretical results. [ABSTRACT FROM PUBLISHER]

Prior mate preference research has focused on the content of mate preferences. Yet in real life, people must select mates among potentials who vary along myriad dimensions. How do people incorporate information on many different mate preferences in order to choose which partner to pursue? Here, in Study 1, we compare seven candidate algorithms for integrating multiple mate preferences in a competitive agent-based model of human mate choice evolution. This model shows that a Euclidean algorithm is the most evolvable solution to the problem of selecting fitness-beneficial mates. Next, across three studies of actual couples (Study 2: n = 214; Study 3: n = 259; Study 4: n = 294) we apply the Euclidean algorithm toward predicting mate preference fulfillment overall and preference fulfillment as a function of mate value. Consistent with the hypothesis that mate preferences are integrated according to a Euclidean algorithm, we find that actual mates lie close in multidimensional preference space to the preferences of their partners. Moreover, this Euclidean preference fulfillment is greater for people who are higher in mate value, highlighting theoretically-predictable individual differences in who gets what they want. These new Euclidean tools have important implications for understanding real-world dynamics of mate selection. [ABSTRACT FROM AUTHOR]