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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 focuses on the distributed resource allocation problem of a second-order multi-agent system with external disturbances. The global network resource equality constraint is considered. A distributed algorithm with an exponential convergence rate is proposed based on an internal model for a known bounded disturbance. By using the active disturbance approach to design a reduced-order extended state observer, the proposed algorithm is modified to reject an unknown external disturbance. Moreover, exponential convergence is achieved through the Lyapunov theory when the disturbance is constant. When the differential of the disturbance is bounded, by adjusting the control parameter of the observer, the proposed algorithm converges to an arbitrarily small neighbourhood of the optimal solution. Finally, our simulation results demonstrate the effectiveness of our proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a class of multi-agent flocking control algorithms are contrived, which can specify communication networking, inter-agent distance and position-pattern subgrouping for second-order particle multi-agent systems, by embedding Olfati-Saber's algorithm with what we call the topological specification matrices/vectors. In other words, by equipping Olfati-Saber's algorithms with more weighting factors, the steady-state formation behaviours in a concerned multi-agent system can be manoeuvered while collision-free flocking is induced. More specifically, we create novel collective potential functions with the topological specification matrices/vectors to evaluate multi-agent dynamics and achieve the expected steady-state features in terms of graph connectivity, distance keeping, position-pattern-related subgrouping and obstacle avoidance. Moreover, formation convergence can be improved by integral specification of position difference. Numerical simulations well demonstrate effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we consider the distributed smooth optimisation problem of multi-agent systems over undirected connected communication topologies. The objective is to minimise the global objective function, which is the sum of local private objective functions, by exchanging and computing local information among each agent or with its neighbourhoods. To avoid continuous communication among agents and reducing communication overheads, we propose an event-triggered distributed optimisation algorithm based on a proportional-integral control strategy. We first show that the developed algorithm does not exhibit Zeno behaviour. Then, it is shown that the proposed algorithm exponentially converges to an exact global minimiser under the restricted secant inequality condition. This condition admits a more general class of the global objective function since it does not require the convexity of the global objective function and the global minimisers are not required to be unique. Theoretical results are illustrated by numerical simulations. [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 discusses a convex optimisation problem with a common set of constraints in the framework of multi-agent systems. Each agent only exchanges information with its neighbours and collaboratively searches for the optimal solution of the global function. To this addressed problem, a distributed multi-step subgradient projection algorithm is developed, where an adaptive event-triggering protocol is designed to govern the information exchange. It is disclosed that the state of each agent representing the estimate of the optimal solution asymptotically converges to one of the optimal solutions under suitably chosen stepsizes and momentum parameters. Simulation results verify that the proposed algorithm has better convergence performance than the standard event-triggered subgradient projection algorithm. In addition, the communication frequency between agents can be effectively reduced to save communication resource consumption. [ABSTRACT FROM AUTHOR]

This paper proposes a class of distributed event-triggered algorithms that solve the average consensus problem in multi-agent systems. By designing events such that a specifically chosen Lyapunov function is monotonically decreasing, event-triggered algorithms succeed in reducing communications among agents while still ensuring that the entire system converges to the desired state. However, depending on the chosen Lyapunov function the transient behaviours can be very different. Moreover, performance requirements also vary from application to application. Consequently, we are instead interested in considering a class of Lyapunov functions such that each Lyapunov function produces a different event-triggered coordination algorithm to solve the multi-agent average consensus problem. The proposed class of algorithms all guarantee exponential convergence of the resulting system and exclusion of Zeno behaviours. This allows us to easily implement different algorithms that all guarantee correctness to meet varying performance needs. We show that our findings can be applied to the practical clock synchronisation problem in wireless sensor networks (WSNs) and further corroborate their effectiveness with simulation results. [ABSTRACT FROM AUTHOR]

This paper considers a multi-agent system with multiple leader agents. All the agents have bounded inputs. Any two agents can communicate only if they are within a fixed distance of each other. Leaders are special agents which generate trajectories autonomously based upon certain objectives. The follower agents must partition into groups such that agents in each group track exactly one leader. Given a particular partition and the resulting information exchange topology, every follower in each group must converge onto their leader's trajectory in min–max time. A special directed spanning tree is selected in each group. Each follower agent in this tree pursues its tail agent using a time-optimal feedback policy. These local feedback laws achieve global min–max time consensus tracking for any general information graph. Further, a decentralised algorithm is proposed to partition the agents so that consensus tracking for all the groups is accomplished in the shortest possible time. [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]

For third-order multi-agent system with time delay based on fixed undirected networks, a theoretical condition is given first that ensures the system to asymptotically converge. Then, to simplify the communication complexity of the multi-agent consensus, a simple yet efficient algorithm is proposed termed the nearest neighbour principle (NNP). NNP leverages partial second-order and third-order neighbour information to largely reduce communication costs and thus contributing to the acceleration of convergence speed. Compared with classical third-order consensus algorithms, the simulation results show that NNP can not only urge the system to achieve consensus but also reduce its convergence time and communication cost. [ABSTRACT FROM AUTHOR]

We address the problem of reaching resilient consensus among a set of agents in the presence of faulty nodes (attacked or noisy). We propose general algorithms, i.e., receiving as inputs a consensus algorithm, the network topology, the initial states, and the number of maximum allowed faulty nodes. These algorithms let the agents identify the set of attacked nodes and correct the consensus value by ignoring the faulty nodes. We prove that if the number of faulty nodes is below the maximum allowed, then each non-faulty agent detects them without false positives. If the inputted discrete-time consensus algorithm has polynomial-time complexity O (C) , then the proposed correction algorithms have polynomial-time complexity O (C n f) (and O (C n) for the detection of faulty nodes), for n nodes, and f maximum allowed faulty nodes. Finally, we show the effectiveness of the algorithms through simulation, pointing out attacking scenarios dealt with our methods, where the state-of-the-art underperformed. [ABSTRACT FROM AUTHOR]

In this study, we investigate the distributed convex optimisation problem of the multi-agent system over an undirected network, in which the global objective function is the sum of all the local cost functions and the local cost function of each agent is only known by itself. In order to save the computation and communication resources, the above optimisation problem is addressed by designing two zero-gradient-sum algorithms with state-based dynamic event-triggered mechanism, where the information communication only occurs at some discrete triggering time instants. The Zeno behaviour of the above event-triggered control scheme is excluded by the existence of the positive minimum inter-event time (MIET). The convergence is proved based on the Lyapunov method. Finally, we illustrate and evaluate the effectiveness of the proposed event-triggered algorithms through numerical experiments on simulated. [ABSTRACT FROM AUTHOR]

Stationary average-tracking problems of unmatched constant reference signals are investigated for first-order multi-agent systems, and an integral-type average-tracking algorithm with delayed-state feedback is constructed. With identical communication delays and different introduced self-delays, sufficient conditions are obtained for the agents converging to the average-value of unmatched reference signals asymptotically by using the frequency-domain analysis method. To our delight, introducing delayed-state feedback improves the robustness to communication delay and unmatched reference signals. Simulation results show the effectiveness of our proposed algorithm. [ABSTRACT FROM AUTHOR]

A distributed adaptive containment control scheme is developed for a class of discrete-time multi-agent systems (MASs) under directed graphs. Each agent's model is described by nonlinear dynamics with unknown parameters that are nonlinearly coupled with the states. To deal with the uncertainties, a criterion function with weighted factor is adopted. By minimising the criterion function, a projection algorithm for each agent is designed to estimate the unknown parameters. Using the estimated parameters, an adaptive controller with only local information is designed for each agent to achieve containment control of MASs. Under the proposed distributed adaptive containment control scheme, it is guaranteed that all the leaders can finally track their reference signals and all the states of followers asymptotically converge to the convex hull formed by leaders (i.e. the convex hull formed by the reference signals). Finally, two simulation examples are given to illustrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]