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This paper proposes a novel importance-based persistent coverage for environments with hotspots. Multiple quadrotors are utilized to maintain coverage while acquiring high-quality information over prioritized regions by lowering the altitude. To achieve this, the approach utilizes distributed communication protocols and cost functions to address the tradeoff between wider coverage and better quality information. The cost functions determine three-dimensional waypoints based on coverage levels, sensor resolution, and Euclidean distance. Voronoi tessellations are used locally for efficient computation and collision avoidance. Trajectories of the quadrotors are generated based on quadrotor dynamics using error functions that take into account state information and desired waypoints. The proposed approach is validated through simulations, comparisons, and experiments for multi-agent systems under quadrotor dynamics. Further, metrics of performance analysis are formulated that show consistent coverage levels and differences to assess the novel importance-based approach. The simulations and experiments demonstrate the feasibility and effectiveness of the approach, with prioritized regions receiving higher sensor resolution while maintaining persistent coverage of the environment. • Novel importance-based persistent coverage for information acquisition over hotspots. • Quadrotor waypoints based on information resolution and decaying environmental costs. • Distributed communication protocols for scalability and low computational complexity. • Real-time importance-based persistent coverage for multi-quadrotor dynamical systems. • Proposed coverage with varying velocities is verified by simulation and experiments. [ABSTRACT FROM AUTHOR]

This paper investigates the fixed-time consensus of second-order multi-agent systems subject to matched and mismatched disturbances. The derivatives of disturbances are assumed to be bounded. A novel integral sliding mode-based observer is presented by utilizing the super-twisting algorithm to estimate the agent states. The estimations can achieve the convergence to the leader in a fixed time. Furthermore, the corresponding fixed-time controller is developed to make the agents converge to the estimated states. As the estimated states converge to the leader, the agents will track the leader states with the help of estimated states. In addition, the overall convergence of multi-agent systems is non-monotonic due to the disturbances. It is theoretically shown that the proposed control approach can maintain a bounded convergence and get the consensus of multi-agent systems in a fixed time. Some simulations are presented to verify the effectiveness of the proposed control protocol. [ABSTRACT FROM AUTHOR]

The event-triggered time-varying formation tracking for a class of second-order multi-agent systems (MASs) subject to a non-cooperative leader is investigated in this paper. First, in the presence of the unknown input of the leader and external disturbances, a distributed observer with adaptive parameters is put forward for followers to estimate the velocity tracking error. Then, based on the estimated tracking error and an auxiliary variable, a finite time formation controller is further constructed, which is updated depending on a pre-designed event-triggered mechanism. As a result, the desired time-varying formation configuration can be realized in finite time with less communication resource consumption. It's noted that the constructed formation strategy doesn't rely on any global information and thus is fully distributed. The stability of the controlled multi-agent system is proved rigorously and it's verified that event-triggered intervals are with a positive lower bound. At last, simulations are carried out to illustrate the effectiveness of the presented algorithm. [ABSTRACT FROM AUTHOR]

This paper solves the finite-time consensus problem for discrete time multi-agent systems (MASs) where agents update their values via linear iteration and the interactions between them are described by signed digraphs. A sufficient condition is presented that the agents can reach consensus on any given linear function of multiple initial signals in finite time, i.e., there exists an eventually positive Laplacian-based matrix associated with the underlying graph. We prove that the linear iterative framework "ratio consensus" developed for unsigned graphs in the literature can be extended to the computation for signed graphs with appropriate modifications. Our method weakens the limitation of the iterative framework on the "marginal Schur stability" of the weight matrix without increasing the computational complexity. Reaching average consensus on unsigned graphs as in the literature is regarded as a special case of our algorithm. Two illustrative examples are presented to demonstrate the correctness of the proposed results. [ABSTRACT FROM AUTHOR]

In this paper, a resilient distributed consensus algorithm is proposed based on multi-agent system to defend against the Byzantine attack. Two attack scenarios including node attack and edge attack are considered and the differences between them are described, in which the edge attack scenario is seldom considered before. The assumption for a network under g -local attacks is relaxed from (2 g + 1) -robust directed graph to (g + 1) -robust one based on a novel filtering method. The convergence and optimality are guaranteed by theoretical analysis, which ensures all the agents without attack can make an agreement and converge to a certain range. The simulation results show that for the two attack scenarios, the proposed algorithm can converge to the optimal solution of the compensation problem and the original problem, respectively. The proposed algorithm is more effective and has faster convergence speed compared with the contrast one. [ABSTRACT FROM AUTHOR]

This paper focuses on the distributed consensus seeking of multi-agent systems (MASs) with discrete-time control updating and intermittent communications among agents. Compared with existing linearly coupled protocols, a nonlinear coupled Zeno-free event-triggered controller is first proposed, which is further to project the static and dynamic triggering mechanisms exploited by using the deadband control method. Then, the node-based nonlinear coupled adaptive event-triggered controller with online self-tuning of time-varying coupling weight and its corresponding to static and dynamic deadband-based event-triggered mechanisms are designed, respectively. The exploited adaptive event-triggered controller does not rely on any global information of interaction structure and is implemented in a fully distributed fashion. In addition, two dynamic proposals not only cover existing static strategies as special cases, but also show that the minimal inter-execution time of dynamic one is not smaller than that of static one. Theoretical analysis shows that the proposed static and dynamic deadband-based event-triggered mechanisms can not only ensure the average consensus with Zeno-freeness, but also achieve the data reduction of communication and control. Finally, the proposed algorithms applied to circuit implementation are corroborated to prove its practical merits and validity. [ABSTRACT FROM AUTHOR]

This paper considers the constrained finite-time consensus control problem for second-order multi-agent systems with leaderless and leader–follower. By using the saturated controller design method and homogenous system theory, a finite-time consensus algorithm for multi-agent systems is proposed. Compared with the existing results, the proposed consensus algorithm makes the closed-loop system not only finite-time convergence but also satisfies the velocity constraints and input saturations. Simulation examples show the effectiveness of the proposed consensus algorithms. [ABSTRACT FROM AUTHOR]

Distributed coordination of multi-agent systems (MASs) has been investigated for many years, and fractional-order calculus has been proved that it can model the dynamics more accurately in certain circumstances. Hence, in this paper, combining the above two aspects, the distributed coordination of fractional-order MASs (FOMASs) is researched, which is a promising topic. Besides, in this paper, the uncertainty, inherent nonlinearity and external disturbances are taken into consideration, aiming at achieving the robust consensus tracking. In particular, the uncertain parameters will be identified from an optimization perspective using artificial bee colony algorithm (ABC). Firstly, to ameliorate the performance of the standard ABC, a hybrid ABC (hABC) incorporating two groups of searching mechanisms is designed, it facilitates the identification of unknown parameters. After obtaining the identified parameters, an efficient distributed nonlinear controller is raised to fulfill the robust consensus tracking. Finally, experiments prove that the designed parameters identification approach can successfully estimate the uncertain parameters with high accuracy, besides the designed control algorithm can robustly control the FOMASs. [ABSTRACT FROM AUTHOR]

This paper addresses the finite-time and fixed-time bipartite average tracking (BAT) problems in multi-agent systems (MASs) subject to bounded disturbances. A novel non-smooth protocol is first constructed on the basis of utilizing the finite-time control method, which can realize the finite-time BAT with structurally balanced graph. Then, the fixed-time BAT problem is dealt with nonlinear fixed-time algorithm, the setting time of which is obtained without depending on initial states. By employing Lyapunov stability theory, some sufficient criteria are obtained to achieve the finite-time and fixed-time BAT for MASs with disturbances. Numerical examples are finally developed for illustration. [ABSTRACT FROM AUTHOR]

In this paper, we address the distributed convex optimization problems for multi-agent systems. In our research, it is assumed that each agent in the multi-agent system could merely interact with its neighbors via a directed graph and is available to its own cost function. We then utilize the push-sum distributed dual averaging (PS-DDA) algorithm to tackle with the distributed optimization problem. However, we consider that there exist subgradient delays in PS-DDA algorithm. The proof of the main result which shows that the PS-DDA algorithm with subgradient delays converges and the error possesses sublinear growth of a rate O (τ 2 T − 0.5) , where T denotes the total amount of iterations, is detailed presented in this paper. Finally, a numerical example is simulated to show the performance of the algorithm we study in this paper. [ABSTRACT FROM AUTHOR]

This paper proposes two kinds of algorithms to achieve privacy-preserving consensus of multi-agent systems over undirected graphs via node decomposition mechanism and homomorphic cryptography technique. Based on the number of neighboring nodes (|Ni|), every agent is decomposed into |Ni| subagents, which are connected as a chain graph. Note that every subagent connects one and only one non-homologous subagent (generated by different agents). Information interaction between non-homologous subagents is encrypted by a homomorphic cryptography algorithm, and homologous subagents exchange information directly. In this regard, the proposed node decomposition mechanism enhances the privacy of the initial values without increasing the computational complexity of encryption. The first privacy-preserving algorithm can achieve the accurate average consensus, which means that the agreement value of every subagent is consistent with the original average consensus value. The second algorithm studies the privacy-preserving scaled consensus problem without a priori knowledge about the underlying graph. Although the final convergence values of subagents do not keep exactly the same, homologous subagents can compute the original group decision value by resorting to the product of the limit value and agent’s degree. Importantly, this algorithm also guarantees the privacy of group decision value of the whole system. Besides, it is proved that the privacy of the initial value can be preserved if the agent has at least one neutral neighbor. [ABSTRACT FROM AUTHOR]

This paper considers a nonsmooth constrained distributed convex optimization over multi-agent systems. Each agent in the multi-agent system only has access to the information of its objective function and constraint, and cooperatively minimizes the global objective function, which is composed of the sum of local objective functions. A novel continuous-time algorithm is proposed to solve the distributed optimization problem and effectively characterize the appropriate gain of the penalty function. It should be noted that the proposed algorithm is based on an adaptive strategy to avoid introducing the primal-dual variables and estimating the related exact penalty parameters. Additional, it is demonstrated that the state solution of the proposed algorithm achieves consensus and converges to an optimal solution of the optimization problem. Finally, numerical simulations are given and the proposed algorithm is applied to solve the optimal placement problem and energy consumption problem. [ABSTRACT FROM AUTHOR]

This paper aims at solving the bipartite containment problem for linear singular multi-agent systems (MASs) with multiple dynamic leaders over general signed digraphs, where each leader can be autonomous or dynamically evolving via interactions with its neighboring leaders. To this end, we first establish some properties for the Laplacian matrices of signed digraphs. Then, three distributed observer-based bipartite containment protocols are proposed using different local output information. Multi-step algorithms for constructing the corresponding proposed protocols are also given. It is shown that under the proposed control protocols, bipartite containment can be achieved for any admissible initial states provided that the underlying signed digraph is weakly connected. Compared to the existing related works, the major contribution of present work is that the developed results are applicable for arbitrary weakly connected signed digraphs, no matter whether they are sign-symmetric or sign-asymmetric and whether they are structurally balanced or unbalanced. Two numerical examples are finally given to demonstrate the validity of our results. [ABSTRACT FROM AUTHOR]

This paper provides a framework of designing distributed model predictive controller to reach consensus of a heterogeneous time-varying multi-agent system, and the dynamics of heterogeneous agents are modeled by double integrators and Euler-Lagrange (EL) equations. Firstly, a DMPC-based consensus algorithm is proposed, where the constraints in the algorithm depend on the heterogeneous dynamics. We prove that the resultant DMPC optimization problem is feasible with the designed controllers, which is stable when the system reaches consensus. To further reduce communication cost and solve the problem with asynchronous discrete-time information exchange, self-triggered mechanism is introduced into the framework. Trigger intervals are alternatively optimized with the control inputs, and the influence on the system performance is analyzed. Numerical examples are provided to verify the effectiveness and advantages of the proposed algorithms. [ABSTRACT FROM AUTHOR]

The problem of simultaneous fault detection and containment control of linear continuous-time multi-agent systems with multi-leaders is firstly addressed in this paper. Distributed detection filters are designed using relative output information and local input information among agents to detect the actuator faults and sensor faults in agents, and H ∞ / H − formulation for this problem is presented in the finite frequency domain. In order to solve the complexity problems caused by multiple leaders in the system, the influence of leaders on the residual signals of followers is taken into account in the design of the proposed detection filter, and through the reasonable design of the controller, the containment control of the followers can also be achieved simultaneously. The controller and filter parameters are obtained by solving the optimization problem with linear matrix inequalities(LMI) constraints. Furthermore, faults are allowed to occur in leaders in this paper, and an effective algorithm is proposed to detect and isolate faults in the system. At last, an adjustable filter structure is proposed to prevent the followers from being influenced by the failure leader, which is of great practical significance. Finally, to illustrate the effectiveness of the proposed methodology, a simulation study is provided. [ABSTRACT FROM AUTHOR]

This paper considers the containment control problem for the discrete-time nonlinear multi-agent systems with unknown parameters and completely unknown control gains. In this paper, the dynamics of each agent are nonlinearly coupled with the neighbors' states. To deal with the unknown parameters and control gains, a projection-type estimation algorithm is designed with the singular value avoided in the estimated control gains. The bounded estimation errors of parameters and control gains are guaranteed by the convergence analysis based on the Lyapunov approach. Then, using the estimated parameters and control gains, a distributed adaptive control protocol with only local information is designed for multi-agent systems. Under the proposed control protocol, the containment of multi-agent systems is guaranteed from a novel viewpoint of the absorbing Markov chain. Furthermore, simulations are carried out to illustrate the effectiveness of the proposed containment control protocol. [ABSTRACT FROM AUTHOR]

This paper studies consensus problems of multi-agent systems with binary-valued communications. Different from most existing works, the agents considered in this paper can only get binary-valued observations of its neighbors’ states with random noises. A consensus algorithm is proposed: first, each agent estimates its neighbors’ states by the recursive projection algorithm; then, each agent designs the control timely based on the estimates. It is proved that the estimates of the states can converge to the true states with a faster convergence rate than that in the parameter estimation. Moreover, the states of the agents can achieve mean-square consensus, and the corresponding consensus speed can achieve $O(1/t)$ under certain conditions. Finally, simulations are given to demonstrate the theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, the distributed constrained optimization problem over a directed graph is considered. We assume that the digraph has a row-stochastic adjacency matrix, which corresponds to the case when agents assign weights to the received information individually. We present an algorithm that converges to the optimal solution even with a time-varying stepsize which is not attenuated to zero. The choice of stepsizes is relatively easy. The usable type of stepsizes is added. The diminishing or constant stepsizes can be used in our algorithm. Equality constraints and set constraints are also considered in this paper. Convergence analysis of the proposed algorithm relies on a conversion theorem between column-stochastic and row-stochastic matrices. Finally, the results of the numerical simulation are provided to verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes scalable, distributed algorithms for solving linear equations by integrating two mechanisms, termed consensus and conservation, in double-layered multiagent networks. The multiagent network considered in this paper is composed of clusters and each cluster consists of an aggregator and a subnetwork of agents. By achieving consensus and conservation through agent–agent communications in the same cluster and aggregator–aggregator communications among different clusters, respectively, distributed algorithms are devised for agents to cooperatively achieve a solution to the overall linear equation. These algorithms outperform existing algorithms, including but not limited to the following aspects—first, each agent does not have to know as much as a complete row or column of the overall equation; second, each agent only needs to control as few as two scalar states when the number of clusters and the number of agents are sufficiently large; third, the dimensions of agents’ states in the proposed algorithms do not have to be the same (while in contrast, algorithms based on the idea of standard consensus inherently require all agents’ states to be of the same dimension). Both analytical proof and simulation results are provided to validate exponential convergence of the proposed distributed algorithms in solving linear equations. [ABSTRACT FROM AUTHOR]

In this paper, we consider the predefined-time distributed optimization problems of homogeneous and heterogeneous linear multi-agent systems under undirected and connected communication topologies, in which, all agents share coupled equality constraint. In order to make all agents converge to the optimal output at predefined-time cooperatively, we propose two distributed algorithms for homogeneous and heterogeneous multi-agent systems according to time-base generator technology and output feedback, therein, the optimal output can make the global cost function reach minimum. In the design of the algorithms, the control gains are not required, which can avoid the requirement of some global information in advance. Furthermore, all agents converge to the optimal output with exponential speed, and we can set the convergence time arbitrarily. Finally, we provide examples to illustrate the effectiveness of the proposed distributed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, the optimal output synchronization of heterogeneous multi-agent systems is studied. To overcome the shortcoming of previous methods that require system model, a data-based optimal control policy is proposed for the output synchronization problem. Besides, the intrinsic equivalence relationship between model-based optimal solution and the proposed data-based optimal solution is proved. To solve the proposed control policy, a reinforcement learning algorithm based on measured input-output data is presented, which is different from existing model-free algorithms based on internal state. According to the algorithm, the optimal output synchronization problem of heterogeneous multi-agent systems can be solved when the full-state vector is unavailable. Finally, the effectiveness of proposed algorithm is verified by simulation examples. [ABSTRACT FROM AUTHOR]

This paper focuses on designing a leader-following event-triggered control scheme for a category of multi-agent systems with nonlinear dynamics and signed graph topology. First, an event-triggered controller is proposed for each agent to achieve fixed-time bipartite consensus. Then, it is shown that the Zeno-behavior is rejected in the proposed algorithm. To avoid intensive chattering due to the discontinuous controller, the control protocol is improved by estimating the sign function. Moreover, a triggering function is proposed which avoids continuous communication in the event-based strategy. Finally, numerical simulations are given to show the accuracy of the theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, the affine formation control problem for multi-agent systems with prescribed convergence time is investigated. Firstly, on the basis of a time-varying scaling function, a distributed continuous control algorithm is designed, under which a stationary affine formation of the nominal configuration is able to be achieved within a prescribed time. Secondly, to track a time-varying formation within the prescribed time, a distributed control protocol is proposed by employing a leader–follower control strategy. Furthermore, the boundary layer technique is adopted to avoid chattering effect. Finally, simulation examples are provided to demonstrated the effectiveness of the proposed design. [ABSTRACT FROM AUTHOR]

This paper researches the output consensus problem of heterogeneous linear multi-agent systems with cooperative and antagonistic interactions. Two fixed-time state compensator control approaches, one static dynamic and the other distributed adaptive dynamic, are considered for heterogeneous systems subject to logarithmic quantization. Then, a fixed-time output regulation control protocol is constructed to cope with the problem of bipartite output consensus and adaptive fixed-time output consensus of heterogeneous systems which is fully distributed without any global information. Besides, the fully distributed adaptive algorithm is employed to calculate the system matrix of leader and it's also effectively eliminated the harmful chattering. Finally, two simulations are carried out to testify the feasibility of theoretical results. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of identifying the interaction geometry of a set of agents, whose collective goal are to achieve consensus under an agreement protocol. By classifying agents into different subsets based on their behavior, as well as introducing the so-called input and output agents, a relationship between the transfer function matrix and the identifiability of system parameters is established. Specifically, two cases are considered. If the set of input agents coincides with the set of output agents, the number of edges in the input agent set, in the complement of input agent set, and between these two sets can be uniquely identified. Thus, the search space of feasible graphs becomes much smaller. The problem can be solved in polynomial time, and an algorithm is provided. Moreover, if all the agents in the system are output agents, parameters of the system can be uniquely identified, and an algebraic method is given to exactly recover the graph topology. A numerical example illustrates the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

For multi-agent system (MAS), most of existing iterative learning control (ILC) algorithms consider about the tracking of reference defined over the whole trial interval, while the point-to-point (P2P) task, where the emphasis is placed on the tracking of intermediate time points, has not been explored. Thus, a distributed ILC method is proposed, in which each agent updates the feedforward control input by learning from the experience of itself and its neighbors in previous repeated tasks to achieve the goal of improving performance. In addition, for the sake of reducing the burden of data transmission in MAS, effective data quantization is essential. In this case, the quantitative measurement of the error of the tracking time points is further used in the ILC updating law. In order to accommodate this requirement, a distributed point-to-point iterative learning control (P2PILC) with tracking error quantization for MAS is first proposed in this paper. A necessary and sufficient condition is presented for the asymptotical stability of the proposed algorithm, and simulation results show the effectiveness of it finally. [ABSTRACT FROM AUTHOR]

In this paper, we consider the distributed optimization problems with linear coupling constraint of general homogeneous and heterogeneous linear multi-agent systems under weighted-balanced and strongly connected digraphs. In order to control all agents converge to the optimal output, we propose distributed control laws, therein, the optimal output can make the global cost function reach minimum. Then we guarantee the convergence of the proposed algorithms by the properties of Laplacian matrix and Lyapunov stability theorem. Furthermore, we extend the result of heterogeneous linear multi-agent system to the case that dynamics of agents are subject to external disturbances, and prove that the algorithm designed by internal model principle can make all agents reach the optimal output exactly. Finally, we provide examples to illustrate the effectiveness of the proposed distributed algorithms. [ABSTRACT FROM AUTHOR]

This paper studies linear stochastic approximation (SA) algorithms and their application to multiagent systems in engineering and sociology. As main contribution, we provide necessary and sufficient conditions for convergence of linear SA algorithms to a deterministic or random final vector. We also characterize the system convergence rate, when the system is convergent. Moreover, differing from non-negative gain functions in traditional SA algorithms, this paper considers also the case when the gain functions are allowed to take arbitrary real numbers. Using our general treatment, we provide necessary and sufficient conditions to reach consensus and group consensus for first-order discrete-time multiagent system over random signed networks and with state-dependent noise. Finally, we extend our results to the setting of multidimensional linear SA algorithms and characterize the behavior of the multidimensional Friedkin–Johnsen model over random interaction networks. [ABSTRACT FROM AUTHOR]

This paper solves the leaderless consensus problem of a class of strict-feedback uncertain nonlinear multi-agent systems with unknown control directions and unknown system parameters. Without using the Nussbaum function approach, a novel control scheme is proposed by means of the switching mechanism. The control algorithm guarantees that consensus errors converge to the origin asymptotically, and the amplitude of the control signals are much smaller compared with those using Nussbaum functions. The simulation results illustrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, the finite-time consensus problem of leader-follower higher-order multi-agent systems with uncertain nonlinearities is studied, where the uncertainties are assumed to satisfy Hölder conditions. To solve this problem, distributed adding a power integrator method, feedback domination technique and integral sliding-mode control are combined together. Firstly, by using distributed adding a power integrator method, a distributed recursive control scheme is proposed. In the recursive control design, based on feedback domination method, the Hölder conditions of the unknown nonlinearities are introduced into the virtual controllers design. Secondly, on the basis of the designed recursive controllers, integral sliding-mode surfaces are designed and then the corresponding distributed sliding-mode controllers are proposed. The designed sliding-mode controllers guarantee that the finite-time leader-follower consensus problem is solved without using a distributed observer. The stability analysis of the closed-loop systems is presented based on finite-time stability theory. Moreover, simulations show the effectiveness of the proposed consensus algorithms. [ABSTRACT FROM AUTHOR]

This paper is concerned with the private distributed optimization problem for multi-agent systems, where all agents cooperatively minimize the sum of individual convex objective functions with the additional requirement that the functions remain differential privacy. Based on the augmented Lagrangian algorithm, an effective distributed algorithm is presented to ensure the privacy by perturbing primal estimates with additive Laplace noises. The proposed algorithm is more likely to have a faster convergence rate compared to the primal-based algorithms, and has less computational burden contrast with the ADMM-based private algorithms. By means of an important factorization of weighted Laplacian matrix, it is proven that the error between optimal solution and ergodic average estimates is bounded, which is related to the privacy level. Within this framework, trade-offs between privacy level and convergence accuracy are analyzed. Finally the effectiveness of the proposed algorithm is illustrated by numerical examples. [ABSTRACT FROM AUTHOR]

Networked predictive control is an effective method to handle time delays and data dropouts in networked control systems, but it is difficult to apply the control strategy to networked multi-agent systems. First, the computational burden will increase exceedingly when the control strategy is applied to networked multi-agent systems. Second, restrictions have to be imposed on the communication topology to implement the control strategy; Third, most existing networked predictive controllers lack robustness to model uncertainties. In this paper, these problems are solved by introducing predicting error to the control scheme. The proposed control strategy not only guarantees consensus and stability of the overall system, but also reduces the computational burden exceedingly. In addition, the proposed algorithm is robust to model uncertainties. At last, the proposed control strategy can be executed in distributed processors, therefore, flexibility and robustness are preserved for the networked multi-agent systems. These features make the control strategy applicable in practice. Digital simulations and experiments based on spacecraft simulators are carried out to verify the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

Time-varying group formation tracking control problems for second-order multi-agent systems with communication delays and multiple leaders are addressed in this paper. The followers are divided into multiple subgroups, and each subgroup is driven to complete the time-varying sub-formation and to track the average state of entire leaders simultaneously. A distributed protocol that considers communication delays and uses only local interaction information is presented. According to Lyapunov-like stability approach, sufficient conditions for the multi-agent systems to realize time-varying group formation tracking with multiple leaders are given. A variable-substitution based method is developed to get the unknown feedback matrix of the protocol and an algorithm is given to summarize the steps to construct the protocol. The theoretical results can be used in the problem of multiple targets enclosed by several subgroups of unmanned aerial vehicles. Finally, numerical examples are applied to verify the feasibility of the proposed results. [ABSTRACT FROM AUTHOR]

Consensus problem in a leader-following multi-agent system with actuator fault is investigated in this paper. By introducing the control allocation and optimal method, a novel distributed controller is proposed to deal with the partial loss of effectiveness and outage fault without reconfiguring the controller. Compared with the existing literatures, the proposed algorithm can guarantee the consensus errors converge to zero asymptotically without the traditional rank assumption. Finally, three simulations are provided to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper investigates the secure state estimation problem of multiagent systems with misbehaving agents. Compared with the existing works which only consider malicious behaviors generated elaborately, a class of faulty behaviors generated randomly is also taken into account. By assuming that faulty behaviors cannot be altered by the adversaries, the notion of orthogonal complement matrix is introduced to characterize the state observability of the multiagent systems with both faulty and malicious agents. It is shown that more misbehaving agents can be tolerated by distinguishing faulty agents from malicious ones. Then, for single agent, two algorithms are provided to estimate the state based on its own value and the measurements obtained from its neighbors. Finally, two simulations are provided to illustrate the correctness and effectiveness of the proposed conditions and methods. [ABSTRACT FROM AUTHOR]

In this paper, the problem of distributed output consensus is investigated for heterogeneous multi-agent systems subject to a class of uncertainties in communication channels. The studied uncertainties, leading to a time-varying communication topology modeled as a Markovian switching communication topology and delayed transit information, may happen discontinuously and affect the consensus behavior of multi-agent systems. To mitigate the effects of such uncertainties on output consensus, a novel switching distributed control protocol is proposed, aiming to guarantee output consensus of the studied networks in the mean square sense. Moreover, an algorithm is given to select the required controller gains based on the solutions to a Lyapunov inequality and a group of regulator equations. Along this framework, a simulation example on the IEEE 24 Bus System is presented to demonstrate the effectiveness of the algorithm and the theoretical results. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of minimizing the global energy cost for multiagent systems to achieve consensus over undirected network topologies. Conventional optimization problems so defined require all-to-all network topologies for interagent communications, which precludes the use of distributed control or otherwise demands that the network be complete. To circumvent this difficulty, we introduce a network approximation (NA) scheme in the optimization criterion, which tends to render the optimization problem to one seemingly over a complete graph network, thus removing the all-to-all communication requirement. With the NA cost, we show that a distributed optimal consensus algorithm always exists for any given connected network topology, which can be determined by solving a single-agent-level parametric algebraic Ricatti equation (PARE). We also investigate the performance of the optimal consensus algorithm, focusing on the minimal energy cost required to achieve consensus optimally, and the speed at which consensus is achieved. Furthermore, for certain more special yet worthy cases, such as single-integrator, double-integrator, and first-order unstable agents, we derive explicit expressions for the energy cost and the consensus speed. It can be seen from these results that the energy cost can be made arbitrarily small for single-integrator and double-integrator systems under the optimal distributed control. On the other hand, for the first-order unstable agents, the energy cost increases and consensus speed decreases monotonically with the value of the agent's real unstable pole. [ABSTRACT FROM AUTHOR]

In this paper, the finite-time time-varying output formation-tracking (FT-TV-OFT) problem of heterogeneous linear multi-agent systems (HL-MASs) is investigated. First, we propose an adaptive finite-time observer for each follower, aiming to estimate not only the leader's state but also the leader's system matrix. Compared with conventional observers, the dependence of the observer parameters on any global information is completely removed. Moreover, exact estimate can be achieved in a finite time. Then, an adaptive algorithm is used to calculate the regulation equations, such that control gains can be obtained. Next, an adaptive distributed controller is constructed to solve the FT-TV-OFT problem. Moreover, we also extend above results to the case of the directed communication topology. Finally, examples are given to demonstrate the effectiveness of the results. [ABSTRACT FROM AUTHOR]

In this paper, a control scheme is proposed for a group of elliptical agents to achieve a predefined formation. The agents are assumed to have the same dynamics, and communication among the agents are limited. The desired formation is realized based on the reference formation and the mapping decision. In the control design, searching algorithms for both cases of minimum distance and tangents are established for each agent and its neighbors. In order to avoid collision, an optimal path planning algorithm based on collision angles, and a self-center-based rotation algorithm are also proposed. Moreover, randomized method is used to provide the optimal mapping decision for the underlying system. Two examples and analyses are presented to demonstrate the effectiveness and potential of the new control scheme. [ABSTRACT FROM AUTHOR]

Existing results on cooperative control of multiagent systems with unknown control directions require that the underlying topology is either fixed with a strongly connected graph or switching between different strongly connected graphs. Furthermore, in most cases the graph is assumed to be balanced. This paper proposes a new class of nonlinear proportional-integral (PI) based algorithms to relax these requirements and allow for unbalanced and switching topologies having a jointly strongly connected basis. This is made possible for single-integrator (SI) and double-integrator (DI) agents with nonidentical unknown control directions by a suitable selection of the distributed nonlinear PI functions. Moreover, as a special case, the proposed algorithms are applied to strongly connected and fixed graphs. Finally, simulation examples are given to show the validity of our theoretical results. [ABSTRACT FROM AUTHOR]

The leader-following consensus problem for linear discrete-time multiagent systems has been studied in the literature using $H_{\infty }$ Riccati inequality design and $H_{2}$ Riccati equation design methods, respectively. These two methods lead to a solvability condition in terms of an inequality that relies on a scaling gain and a fixed weighting vector called nominal weighting vector. In this paper, we further study this problem by proposing a more general class of distributed state feedback control laws, which not only depends on a scaling gain, but also a set of weighting vectors. We first establish a spectral property of a weighted graph matrix, which will be instrumental in solving the problem. Then, we present a solvability condition based on a modified algebraic Riccati equation, which is somehow more general than $H_{\infty }$ and $H_{2}$ Riccati methods, in that the solvability condition can be made satisfied by tuning the weighting vector. We show that our solvability condition is also necessary for single-input follower systems and is always satisfied for leader systems without exponentially growing modes. Moreover, in case the solvability condition is not satisfied under the nominal weighting vector, we show that our solvability condition can always be made satisfied by choosing a weighting vector other than the nominal one for multiagent systems over acyclic digraphs. [ABSTRACT FROM AUTHOR]

The circle formation problem for a group of mobile agents with limited interaction range is addressed in this paper. Distributed switching control laws are developed for multiagent systems to form prescribed formations from any initial configurations on a circle. It is shown that the desired circle formation can be achieved if a parameter depending on the agents’ interaction range is larger than the prescribed angular distance from each agent to its immediate counterclockwise agent. When the agents’ interaction range is not sufficiently large, the formation error of each agent converges to a limit, and an upper bound and a lower bound on the limit value are provided. It is also shown that under the proposed control laws, the mobile agents’ spatial order on the circle is always preserved and thus collision between mobile agents is avoided. [ABSTRACT FROM AUTHOR]

The distributed convex optimization problem is studied in this paper for any fixed and connected network with general constraints. To solve such an optimization problem, a new type of continuous-time distributed subgradient optimization algorithm is proposed based on the Karuch–Kuhn–Tucker condition. By using tools from nonsmooth analysis and set-valued function theory, it is proved that the distributed convex optimization problem is solved on a network of agents equipped with the designed algorithm. For the case that the objective function is convex but not strictly convex, it is proved that the states of the agents associated with optimal variables could converge to an optimal solution of the optimization problem. For the case that the objective function is strictly convex, it is further shown that the states of agents associated with optimal variables could converge to the unique optimal solution. Finally, some simulations are performed to illustrate the theoretical analysis. [ABSTRACT FROM AUTHOR]

Abstract We address the leader-following tracking consensus issue for a class of linear multi-agent systems (MASs) via dynamic event-triggered (DET) approaches in this paper. The DET communication mechanism is introduced by an additional internal dynamic variable, and is developed to schedule agents' data transmission. State observers are also employed to tackle the scenario wherein inner information of follower agents are not available for measurement. And then, state-based and observer-based distributed control proposals are proposed on the basis of dynamic event-triggered mechanism (DETM), respectively. To avoid continuous measurement information monitor, we present a technical approach for generation of the combinational information from their own neighboring agents only at event instants. The stabilities of the resulting closed-loop systems, both state-feedback one and output-feedback one, are rigorously analyzed in theory, and it is proven that all signals in the closed-loop system are bounded and Zeno behavior is also excluded. Simulation examples are presented to illustrate the theoretical claims. [ABSTRACT FROM AUTHOR]

Abstract This paper investigates group consensus for leaderless multi-agent systems with non-identical dynamics. The consensus protocol is put forward in the form of the distributed event-triggered control subject to saturation, which depends on information from neighboring agents at event-triggered instants. In order to exclude the Zeno behavior and save resources, the given event-triggered condition is detected only at discrete sampling times, where the sampling intervals can be variable. Based on the graph theory, Lyapunov–Krasovskii functional method and by adopting the free-weighting matrix technique, some sufficient group consensus criteria in terms of linear matrix inequalities are derived. Furthermore, optimization problems aiming at maximizing the event-triggered parameter and the consensus region are proposed. Finally, numerical simulations illustrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

This paper studies the rendezvous strategy for a group of unicycle systems with connectivity preservation and collision avoidance. Based on the method of potential functions, a novel distributed control algorithm is proposed for all unicycles. By tuning the design parameters, the unicycles finally aggregate so that the average of the distances is bounded by a pre-specified positive number. It is proved that the connectivity of a minimum spanning tree in the initial topology is guaranteed. The result is then extended to multiple unicycles with heterogenous input disturbances. Potential function is further modified to handle the disturbances. Illustrative example is presented to show the improvements and effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

Predefined mymargin finite-time output containment control problem for nonlinear multi-agent systems with multiple dynamical leaders under directed topology is investigated, where the outputs of followers can converge to the predefined convex hull formed by the multiple leaders within a finite time, and the leaders can have unknown control inputs. Firstly, for the directed topological structure among the followers, a distributed adaptive observer is designed to estimate the whole states of all the leaders under the influences of the leaders’ unknown inputs. By utilizing Hardy’s inequality and common Lyapunov theory, the finite-time convergence of the proposed observer is proved. On the basis of this conclusion, a predefined distributed containment control protocol including the desired convex combinations of the leaders is developed for each follower by using the given weights. Then an algorithm is proposed to design the control parameters in the proposed containment control protocol. With the help of the output regulation theory, the finite-time output containment criterion for nonlinear multi-agent systems in the presence of the leaders’ unknown inputs is derived. Finally, a numerical simulation example is presented to demonstrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

This paper considers the problem of the leader-following consensus of generally nonlinear discrete-time multi-agent systems with limited communication channel capacity over directed fixed communication networks. The leader agent and all follower agents are with multi-dimensional nonlinear dynamics. We propose a novel kind of consensus algorithm for each follower agent based on dynamic encoding and decoding algorithms and conduct a rigorous analysis for consensus convergence. It is proved that under the consensus algorithm designed, the leader-following consensus is achievable and the quantizers equipped for the multi-agent systems can never be saturated. Furthermore, we give the explicit forms of the data transmission rate for the connected communication channel. By properly designing the system parameters according to restriction conditions, we can ensure the consensus and communication efficiency with merely one bit information exchanging between each pair of adjacent agents per step. Finally, simulation example is presented to verify the validity of results obtained. [ABSTRACT FROM AUTHOR]

Purpose – Rigidity of formation is an important concept in multi-agent localization and control problems. The purpose of this paper is to design the control laws to enable the group to asymptotically exhibit the flocking motion while preserving the network rigidity at all times. Design/methodology/approach – The novel approach for designing control laws is derived from a smooth artificial potential function based on an undirected infinitesimally rigid formation which specifies the target formation. Then the potential function is used to specify a gradient control law, under which the original system then becomes an orderly infinitesimally rigid formation. Findings – The strong relationship between the stability of the target formation and the gradient control protocol are utilized to design the control laws which can be proved to make the target formation stable. However, the rigidity matrix is not utilized in the design of control law. Future research will mainly focus on formation control with the relationship of rigidity matrix. Originality/value – The value of this paper is focused on the control laws design and the control laws could enable the group to asymptotically exhibit the flocking motion while preserving the network rigidity at all times. Also the detailed simulations and experiments are given to prove that the novel approach is available. [ABSTRACT FROM AUTHOR]

The PageRank algorithm employed at Google assigns a measure of importance to each web page for rankings in search results. In our recent papers, we have proposed a distributed randomized approach for this algorithm, where web pages are treated as agents computing their own PageRank by communicating with linked pages. This paper builds upon this approach to reduce the computation and communication loads for the algorithms. In particular, we develop a method to systematically aggregate the web pages into groups by exploiting the sparsity inherent in the web. For each group, an aggregated PageRank value is computed, which can then be distributed among the group members. We provide a distributed update scheme for the aggregated PageRank along with an analysis on its convergence properties. The method is especially motivated by results on singular perturbation techniques for large-scale Markov chains and multi-agent consensus. A numerical example is provided to illustrate the level of reduction in computation while keeping the error in rankings small. [ABSTRACT FROM AUTHOR]