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This paper considers a class of multi-agent distributed convex optimization with a common set of constraints and provides several continuous-time neurodynamic approaches. In problem transformation, l 1 and l 2 penalty methods are used respectively to cast the linear consensus constraint into the objective function, which avoids introducing auxiliary variables and only involves information exchange among primal variables in the process of solving the problem. For nonsmooth cost functions, two differential inclusions with projection operator are proposed. Without convexity of the differential inclusions, the asymptotic behavior and convergence properties are explored. For smooth cost functions, by harnessing the smoothness of l 2 penalty function, finite- and fixed-time convergent algorithms are provided via a specifically designed average consensus estimator. Finally, several numerical examples in the multi-agent simulation environment are conducted to illustrate the effectiveness of the proposed neurodynamic approaches. [ABSTRACT FROM AUTHOR]

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

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

Traffic guidance and traffic control are effective means to alleviate traffic problems. Formulating effective traffic guidance measures can improve the utilization rate of road resources and optimize the performance of the entire traffic network. Assuming that the traffic coordinator can capture traffic flow information in real-time utilizing sensors installed on each road, we consider the strong resilience constraints to construct an optimal selection problem of balanced flow in the traffic network. Based on multi-agent modeling, each agent has access to the corresponding traffic information of each link. We design a distributed optimization algorithm to tackle this optimization problem. In addition to the inherent advantages of distributed multi-agent algorithms, the communication topology among the sensors is allowed to be time-varying, which is more consistent with reality. To prove the effectiveness of the proposed algorithm, we also give a numerical simulation in the multi-agent environment. It should be reiterated that the optimization problem studied in this paper mainly focuses on traffic managers' perspectives. The goal of the studied optimization problem is to minimize the overall cost of the traffic network by adjusting the optimal equilibrium traffic flow. This study provides a reference for solving congestion optimization in today's cities. • Formulate an equilibrium flow selection problem with resilience constraints. • The distributed algorithm allows the communication among agents to be time-varying. • Communication graph among agents allows for being B-connected. [ABSTRACT FROM AUTHOR]

This paper provides a solution to the problem of safe region formation control with reference velocity tracking for a second-order multi-agent system without velocity measurements. Safe region formation control is a control problem where the agents are expected to attain the desired formation while reaching the target region and simultaneously ensuring collision and obstacle avoidance. To tackle this control problem, we break it down into two distinct objectives: safety and region formation control, to provide a completely distributed algorithm. Region formation control is modeled as a high-level abstract objective, whereas safety and actuator saturation are modeled as a low-level objective designed independently, without any knowledge of the former, and being minimally invasive. Our approach incorporates connectivity preservation, actuator saturation, safety considerations, and absence of velocity measurement from other agents with second-order system dynamics which are important constraints in practical applications. Both internal safety for collision avoidance among agents and external safety for avoiding unsafe regions are ensured using exponential control barrier functions. We provide theoretical results for asymptotic convergence and numerical simulation to show the approach's effectiveness. [ABSTRACT FROM AUTHOR]

This paper proposes a distributed resilient consensus framework consisting of a distributed model predictive control (DMPC)-based consensus protocol and a new distributed attack detection mechanism, aiming to effectively handle the general linear constrained MAS under adversarial attacks. Based on the historical information from neighbors and a resilience set , we can evaluate the reliability of communication links via the attack detection mechanism. The proposed detection mechanism can significantly reduce the requirement on the network robustness compared with the well-known mean-subsequence-reduced (MSR) algorithms. The resilient consensus of general linear constrained MAS with F -locally adversarial attacks is achieved when the communication network is (F + 1)-robust. Furthermore, the DMPC-based resilient consensus framework exhibits the following key feature: the constrained consensus performance is optimized by minimizing a set of control variables. We demonstrate that the recursive feasibility of the associated DMPC optimization problem and the resilient consensus convergence of the constrained MAS can be guaranteed. Finally, the effectiveness of the proposed framework is illustrated through simulation experiments. [ABSTRACT FROM AUTHOR]

In this paper, a fully distributed unknown input observer is designed, which can give an exact state estimate for linear time-invariant systems with unknown inputs. Specifically, it is a group of sensors with complementary measurement capabilities, in which each one serves as a local observer and has communication links with neighbors. Such networked sensor nodes are designed to cooperate by taking complementary actions, so that each one can estimate the full state of the plant exactly. The fully distributed design in this paper requires no global information such as the communication topology, collective measurement capability, or bound of the unknown input. [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]

In this paper, we propose a novel adaptive consensus-based learning algorithm for automated and distributed web hacking. We aim to assist ethical hackers in conducting legitimate penetration testing and improving web security by identifying system vulnerabilities at an early stage. Ethical hacking is modeled as a capture-the-flag style task addressed within a distributed reinforcement learning framework. To achieve our goal, we employ interconnected intelligent agents that interact with their copies of the environment simultaneously to reach the target. They perform local information processing to optimize their policies and exchange information with neighboring agents. We propose a novel adaptive consensus scheme for inter-agent communications, which enables the agents to efficiently share network-wide information in a decentralized manner. The scheme dynamically adjusts its weights based on heuristics, involving both recency and frequency metrics of actions selected at a given state by an individual agent, similar to eligibility traces. We extensively analyze the convergence properties of our algorithm and introduce a new communication scheme design. We demonstrate that this design ensures the fastest convergence to the desired asymptotic values under the general setting of asymmetric communication topologies. Additionally, we provide a comprehensive review of the current state of the field and propose a web agent model with improved scalability compared to existing solutions. Numerical simulations are conducted to illustrate the key characteristics of our algorithm. The results demonstrate that it outperforms both non-cooperative and average consensus schemes. Moreover, our algorithm significantly reduces hacking times when compared to baseline algorithms that rely on more complex models. These findings offer valuable insights to system security administrators, enabling them to address identified shortcomings and vulnerabilities effectively. • A novel adaptive consensus-based distributed algorithm for penetration testing is proposed. • Ethical hacking task is modeled within the reinforcement learning framework. • Extensive simulations show the proposed algorithm outperforms others in reducing hacking time. • The obtained results offer valuable insights to system security administrators in a fast manner. [ABSTRACT FROM AUTHOR]

This paper investigates the practical fixed-time consensus problem for a multi-agent system in networks with an undirected topology. Both node-based and edge-based fully distributed adaptive protocols are proposed to achieve the practical fixed-time consensus for the single integrator-type multi-agent systems, i.e., fixed-time attractiveness of a residual set as well as asymptotic consensus. Most notably, the settling time estimate can be computed without using any global information, which is distinctive from the existing fixed-time protocols. Furthermore, a scaling technique is applied for the proposed protocol such that the fixed-time attraction region can be contracted by only tuning a scaling parameter and the original settling time estimate remains unchanged. Finally, a numerical simulation example is given to validate the proposed protocols. [ABSTRACT FROM AUTHOR]

This paper solves the coordinated optimal consensus problem for second-order multi-agent systems without velocity information from the distributed perspective. Particularly, all the agents are driven towards a consensus at an optimal solution inside the intersection of multiple feasible sets that minimizes a global target function aggregated by a group of convex local ones. Each local target function is exclusively assigned to a respective agent. Also, each feasible set constructed by inequality and set constraints privately belongs to a respective agent. Resorting to a novel auxiliary dynamics, a velocity-free distributed coordinated optimal control strategy is proposed without relying on global topology information. Using convex theory under the Lyapunov framework, it is demonstrated that the concerned coordinated optimal control objective is fulfilled by implementing the proposed distributed control strategy despite no velocity information. Examples are finally given to confirm the control effectiveness. [ABSTRACT FROM AUTHOR]

• The data-driven inverse optimization corrects the DR execution online. • The proposed strategy is realized in a distributed manner and free of initialization. • This paper provides rigorous stability proof for the proposed optimization. • D-PPDS algorithm solves the DR optimization with global inequality constrain. This paper provides an insight into the demand response (DR) optimization in distribution markets consisting of a retailer and multiple demand response aggregators (DRA), where a retailer determines DR incentives based on power consumption profile. Conventional DR optimization with global states and constraints is intractable to be implemented in a distributed framework, which restricts the application feasibility and the potential profit of DR. To handle these limitations, we design a multi-agent architecture for distributed demand response (DDR). An online data-mining method is developed to identify the characteristics of DR. A leader–follower structure decomposes the original problem into a leader problem with global variables and aggregators of sub-problems, where discrete singular consensus is designed to broadcast the leader's strategy to followers in real-time. The distributed perturbation primal–dual sub-gradient (D-PPDS) algorithm is proposed to solve the DDR problem with global inequality constraints in a completely distributed fashion. The proposed DDR strategy is tested by an actual case. The simulation results demonstrate that the asynchronous D-PPDS algorithm can obtain the near-optimal solution of the problem with global inequality constraints, and is robust against delay or plug-and-play. [ABSTRACT FROM AUTHOR]

Many works on multi-agent consensus protocol designs rely on the full states of nodes or the node- observer-based procedures, under both of which the nodal information plays a key role in the consensus design. In this paper, we propose an edge-based augmented system approach for second-order nodal consensus, based on the edge loop transfer matrix of the closed-loop multi-agent system, which is defined by cutting the loop at the inputs of the edges. Each agent is allowed to be of single-output and additional poles and zeros are introduced into edges to construct the channel filter with previous data stored in the memories. We firstly focus on the consensus design by reserving two terms of the non-recursive filter and then propose a simplified design approach exploiting a single parameter, which is of low complexity and can be readily implemented. We also discuss the consensus design by using the non-recursive channel filter with higher-order realizations. Finally, as an extension, we discuss the consensus design based on the first-order recursive channel filter. The effectiveness of the proposed method is verified by the illustrative simulations. [ABSTRACT FROM AUTHOR]

This paper solves the simultaneous localization and formation (SLAF) problem for a multi-agent system moving in 2D plane. The multi-agent system consists of leaders who have the knowledge of their absolute positions in the global coordinate frame, and followers who do not know their absolute positions but have angle-only measurements and communication with respect to their neighboring agents. The aim of SLAF is to simultaneously localize and control the followers such that a desired formation among the leaders and followers can be achieved by using locally available sensing and communication information. To handle the challenging situation where the formation becomes unlocalizable at some nongeneric configurations, a perturbation-based SLAF algorithm is proposed such that the SLAF task can be achieved with an asymptotic convergence. To meet different tasks' requirements, three types of distributed SLAF algorithms are designed for the followers when the leaders are static, move with constant, or time-varying velocities, respectively. The effect of measurement noises, extension to other types of sensor measurements, requirement on agents' coordinate frames, collision and collinearity avoidance are also discussed. To validate the theoretical results, simulation examples corresponding to the discussed scenarios are carried out. [ABSTRACT FROM AUTHOR]

Existing consensus designs require that the information of local control laws is open access. This is undesirable whenever the privacy of local interactions is concerned. In this paper, a different framework of the consensus protocol design with the consideration of authority management of accessing local controllers is provided, by using an edge-based implementation. In the scenario of the proposed designs, each local controller is realized by a parallel connection of a public element and a private element. The public elements are uniform and open access. However, the private elements are allowed to be significant nonuniform and are only known by the pairs of agents who share them. For both continuous-time multi-agent systems and their sampled-data counterparts, we provide the design criterions for the partial public protocols under both static and dynamic public elements, respectively. By exploring the edge sensitivity design approach, it is shown that the consensus can be achieved under the proposed protocols if the private control laws satisfy certain constraints dependent on the public ones and the network topologies. Numerical examples are also provided to illustrate the proposed design criterions. [ABSTRACT FROM AUTHOR]

To achieve large-scale generalized demand-side resources to participate in the "peak-shaving and valley-filling" of the power system, a distributed cooperative control strategy for discrete-time linear multi-agents considering historical data is proposed, which comprehensive consider the changes in load commands, topology, multi-objective function coefficients, communication delays and input delays. This paper combines the distributed multi-agent system with the hierarchical architecture to solve the problems of extensive data calculations in centralized control, which may easily cause communication channel blockage and delay. Considering the cost of changing the generalized demand-side resources and the user's comfort, the pre-learning historical data storage strategy based on K-means clustering is adopted before the actual task, and the idea of transfer learning is introduced into the consensus algorithm. Finally, through experimental statistics, the number of iteration steps of the consensus algorithm is reduced and the effectiveness of the proposed algorithm is proved. [ABSTRACT FROM AUTHOR]

A formation flight control algorithm for a multi-agent system composed of a group of perturbated quadrotors is thoroughly investigated in this paper. Both theoretical and practical aspects are addressed in detail to design and implement the control algorithm in a distributed fashion among the networked agents. By adopting the sliding mode framework: (i) A novel Nonlinear Homogeneous Nonsingular Terminal Sliding Surface (NHNTSS) is designed. To ensure Global Asymptotic Stability (GAS) as well as fixed-time convergence of the states, the sliding surface design skillfully employs the generalized weighted homogeneity theory. (ii) A novel Distributed Fixed-Time Continuous Nonsingular Control Protocol (DFCNCP) is proposed for the position-loop of each quadrotor agent by utilizing the designed NHNTSS. Moreover, the control scheme comprises a disturbance observer to compensate for the lumped disturbances affecting the position dynamics and estimate the unmeasurable linear velocities of the quadrotors. Overall, the synthesized controller can drive the formation tracking errors into close vicinity of the origin in fixed-time uniformly to the values of the Initial Conditions (ICs), i.e., initial positions of the quadrotors in the 3D state-space. The mathematical proofs based on the bi-limit approximation in the existing related works can only indicate that the system is guaranteed to be fixed-time stable without any estimation of the settling (convergence) time. In contrast, rigorous stability analyses are conducted in the present work by virtue of algebraic Lyapunov tools, i.e., Algebraic Lyapunov Equation (ALE) and Lyapunov Quadratic Function (LQF). Thus, an expression of the upper-bound on the settling-time is given explicitly. Numerical simulations and real experiments in the form of exhaustive comparative studies are carried out to validate the findings of this research work. Overall, the obtained results confirm the superiority of the proposed control strategy in terms of uniform fast fixed-time convergence, strong disturbance rejection, chattering alleviation, and control precision. [ABSTRACT FROM AUTHOR]

This paper deals with the distributed iterative learning control (ILC) problem of leaderless consensus in a network of heterogeneous nonlinear agents. For a general case that the topology graph is dynamically changing with respect to both iteration and time axes, an ILC-based consensus protocol is designed for each agent by utilizing its control input and neighboring information from the last iteration. It is shown that under a basic joint spanning tree condition, the states of all agents can exponentially agree on a common trajectory along the iteration axis. Interestingly, by the appropriate design of the agents' dynamics, it is found that the consensus trajectory and the network state can approach the unique optimal point of a convex optimization problem as the time evolves. Simulations demonstrate the validity of the proposed algorithms and theoretical analysis. [ABSTRACT FROM AUTHOR]

In this paper, two distributed and communication-efficient algorithms based on the multi-agent system are proposed to solve a system of linear equations with the Laplacian sparse system matrix. One algorithm is based on the gradient descent method in optimization. In this algorithm, the agents only share partial information instead of all of their collective state vectors to save significant communication. The other algorithm is obtained by approximating Newton's method for a faster convergence rate. Although it requires twice as much communication as the first one, it is still communication-efficient given the low dimension of the information shared among agents. The convergence at a linear rate is proved for both algorithms, and a comprehensive comparison of their convergence rate, communication burden, and computation costs is also performed. The proposed algorithms can be applied to various systems to solve those problems that can be modeled as a system of linear equations with a Laplacian sparse system matrix. Simulation results with the electric power system illustrate their effectiveness. • Develop communication-efficient distributed algorithms to solve a system of linear equations. • Prove the convergence of the algorithms. • Apply the algorithms to power flow problems in power systems. [ABSTRACT FROM AUTHOR]

In this paper, the consensus problem of a continuous-time uncertain multi-agent system (MAS) with time-varying interval uncertainties in the system matrix and unknown initial states is investigated via output feedback. Relating only to the interval bounds on the uncertainties, a reduced-order framer, which includes two Luenberger-like observers, is first derived. Then, a distributed control algorithm relying only on the information of the framers associated with each agent and its neighbors is proposed. It is shown that the control algorithm can render the reduced-order framer to be an exponential reduced-order interval observer, and simultaneously drive the uncertain MAS to reach robust consensus exponentially. Finally, numerical simulations for a large-scale MAS are used to verify the theoretical results. [ABSTRACT FROM AUTHOR]