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Based on game theory, this paper considers the consensus problem of second-order hybrid multiagent systems (HMASs) in a leader–follower framework, which includes continuous-time multiagents and discrete-time multiagents. Firstly, the competitive relationship between two groups is modeled as a multi-player game type. To achieve consensus, we design different cost functions according to the rules of the game and assume that each player has a global purpose of minimizing their own cost. Moreover, the players adjust their states at the next moment according to the Nash equilibrium solution. Then, using matrix theory and stability theory, we analyze the necessary and sufficient conditions for the consensus problem of the second-order HMASs with different leaders, and show that the control parameters (sampling interval and feedback gain) and the eigenvalues of the network topology have essential impact on the consensus of the system. Finally, the validity of the results is tested by simulation examples. [ABSTRACT FROM AUTHOR]

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 considers the distributed prescribed-time optimization problem of multi-agent systems (MASs). Considering the strongly convex function of time-invariant for each agent, the two-stage distributed prescribed-time optimization algorithm is designed based on the idea of zero-gradient-sum. Meanwhile, in order to save system resources, the event-triggered control mechanism is introduced into the algorithm in this paper. In the first stage, the distributed prescribed-time event-triggered algorithm is proposed to minimize the local objective functions of each agent at the prescribed-time interval. In the second stage, the algorithm is driven to optimize the global cost function while maintaining the gradient sum of all local cost functions to zero. The criteria for achieving the consensus and optimization of MASs are obtained by using Lyapunov stability theory and optimization theory. Moreover, it is proved in detail that using the two triggering functions will not result in Zeno behavior. The numerical example is given to demonstrate the correctness of the theoretical analysis and the effectiveness of the control algorithms. [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 investigates the fixed-time cluster optimization problem of first-order multi-agent systems (MASs) under directed detail-balanced networks, in which the optimization objective is a linear combination of local objective functions. Two piecewise distributed control protocols are proposed to solve the optimization problem with time-invariant and time-varying objective functions, respectively. Under the two proposed piecewise control protocols, the fixed-time cluster optimization problem can be solved on the basis of maintaining the cluster consensus unchanged. Furthermore, the initial states of all the agents need not be restricted to the minimum of their local cost functions. Finally, we provide two examples to illustrate the effectiveness of the proposed control protocols. [ABSTRACT FROM AUTHOR]

In this paper, we study the cooperation problem over a group of discrete-time nonlinear dynamically decoupled multi-agent systems (MAS). A distributed model predictive control (DMPC) scheme is proposed in the event-triggered context. Agents cooperate through a coupled cost function subject to input constraints. From the practical perspective, the additive disturbances are taken into account in the controller design. Using the contraction theory in the framework of Riemannian manifolds, a novel constraint is constructed in the DMPC optimization problem to provide the capability of disturbance rejection. Moreover, the event-triggered mechanism is introduced for saving computational and communicational resources. The event-triggering condition is developed by checking the Riemannian distance between the actual and optimal state trajectories. The stability of the closed-loop system and recursive feasibility of the DMPC scheme, thereafter, are rigorously analyzed. In particular, the stability analysis is built upon the contraction theory, which distinguishes this work from the existing results using the conventional Lyapunov theory. It is shown that the recursive feasibility is guaranteed if the additive disturbances are bounded and the event-triggering condition is properly designed. The numerical simulation results demonstrate the effectiveness of the proposed algorithm. [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, 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]

Privacy-preserving methods play an indispensable role in protecting agents' cost functions from being disclosed during the dispatch process. In this paper, we aim at resolving Economic Dispatch Problems (EDPs) over multi-agent systems in a fully decentralized and privacy-preserving manner. Based on both row- and column-stochastic weight matrices, we first design a fully decentralized algorithm without any privacy-masking techniques, namely F-Deed, to resolve a class of structured EDPs over directed networks. We further consider an adverse case that potential attackers including both internal honest-but-curious adversaries and external eavesdroppers try to infer or steal the local cost functions of all agents to achieve their malicious goals. To protect agents' local cost functions against differential attacks, a differentially private algorithm, dubbed DPF-Deed, is developed via enhancing F-Deed with local differential privacy (LDP). To attain LDP, DPF-Deed decomposes the gradient tracker of F-Deed into two sub-trackers, with one of them invisible to any other agents, and masks a decomposed gradient with Laplace noise. Under standard assumptions, theoretical analysis validates that DPF-Deed can achieve linear convergence and an explicit trade-off between LDP and convergence accuracy, which are derived by analyzing the contraction relationships among the network consensus error, the optimal gap, and the gradient-tracking error. Theoretical results are validated by case studies for (dynamical) EDPs based on modified IEEE-14 and IEEE-118 bus systems. [ABSTRACT FROM AUTHOR]

This paper focuses on the formation control problem under communication constraints for leader-follower cyber-physical multi-agent systems (CPMASs). The introduction of a leader-follower structure motivates the design of a distributed predictive formation control (DPFC) protocol for CPMASs, which features active compensation for constraints in the network with the aim of achieving formation consensus and stability. The cost functions are designed separately for leader and followers, enabling the output coordination of the leader and followers under the consideration of the formation configuration. The necessary and sufficient condition for stability and consensus is derived through the detailed analysis of closed-loop CPMASs with DPFC scheme. The control performance of the designed DPFC scheme is validated by the presented numerical simulations, which is extended to a formation control experiment of three-degree-of-freedom air-bearing-simulator (ABS) systems. • A DPFC protocol is designed to actively compensate the communication constraints. • The DPFC scheme can achieve consensus and stability for leader-follower CPMAS. • The cost functions considering the dynamic characteristics are designed respectively for the leader and followers. • The necessary and sufficient condition for reaching consensus and stability simultaneously with DPFC scheme is given. • The 3-DOF air-bearing simulators are used to verify the effectiveness of the DPFC scheme. [ABSTRACT FROM AUTHOR]