Cooperation-competition Networks Research Articles

Group consensus for fractional-order heterogeneous multi-agent systems under cooperation-competition networks with time delays

The issue of group consensus for heterogeneous fractional-order multi-agent systems under the cooperation-competition networks with time delays is investigated in this paper. Novel group consensus control protocols with input and communication delays are designed based on cooperative-competitive interaction. The considered multi-agent systems consists of fractional order dynamics with the single integrator and the double integrator, and the speed of agents is not known. The matrix theory, frequency domain approach and graph theory are used to figure out the sufficient conditions for group consensus under the switching and fixed topology, respectively. Finally, numerical simulation examples are given to verify the correctness of the theoretical results.

Multi-Agent Bipartite Flocking Control Over Cooperation-Competition Networks With Asynchronous Communications

Multi-Agent Bipartite Flocking Control Over Cooperation-Competition Networks With Asynchronous Communications

Scaled Consensus of Multi-Agent Systems in the Cooperative-Competitive Networks

Scaled Consensus of Multi-Agent Systems in the Cooperative-Competitive Networks

The Weighted Group Consensus for a Kind of Discrete Heterogeneous Multi-Agent Systems With Time Delay and Packet Loss in Cooperative-Competitive Networks Based on Self-Adaptive Controller

The Weighted Group Consensus for a Kind of Discrete Heterogeneous Multi-Agent Systems With Time Delay and Packet Loss in Cooperative-Competitive Networks Based on Self-Adaptive Controller

Global precise consensus tracking control for uncertain multiagent systems in cooperation-competition networks

The multiagent systems (MASs) play a crucial role in practical applications, and the global precise control of MASs under cooperative-competitive networks presents significant challenges. This paper addresses the global precise consensus tracking control problem for uncertain MASs in cooperation-competition networks. A novel framework of distributed event-based prescribed-time observer (EPTO) is proposed for the followers, which allows to accurately estimate the leader's state within a prescribed time interval. Moreover, the design of event-triggered conditions for each EPTO reduces the communication load between neighboring agents by avoiding the continuous transmission of neighbor states. Using the observed values after triggering within the backstepping method framework, a global bipartite control scheme is then developed to expedite the convergence time, enhance the convergence performance, and perform precise tracking control for high-order uncertain MASs. The distinctive feature of the proposed global bipartite control scheme lies in the design of a novel controller, which improves the semi-global control result of the existing schemes. Finally, two simulation examples are presented to demonstrate the effectiveness of the EPTO-based bipartite control scheme.

Learning inverse kinematics using neural computational primitives on neuromorphic hardware

Current low-latency neuromorphic processing systems hold great potential for developing autonomous artificial agents. However, the variable nature and low precision of the underlying hardware substrate pose severe challenges for robust and reliable performance. To address these challenges, we adopt hardware-friendly processing strategies based on brain-inspired computational primitives, such as triplet spike-timing dependent plasticity, basal ganglia-inspired disinhibition, and cooperative-competitive networks and apply them to motor control. We demonstrate this approach by presenting an example of robust online motor control using a hardware spiking neural network implemented on a mixed-signal neuromorphic processor, trained to learn the inverse kinematics of a two-joint robotic arm. The final system is able to perform low-latency control robustly and reliably using noisy silicon neurons. The spiking neural network, trained to control two joints of the iCub robot arm simulator, performs a continuous target-reaching task with 97.93% accuracy, 33.96 ms network latency, 102.1 ms system latency, and with an estimated power consumption of 26.92 μW during inference (control). This work provides insights into how specific computational primitives used by real neural systems can be applied to neuromorphic computing for solving real-world engineering tasks. It represents a milestone in the design of end-to-end spiking robotic control systems, relying on event-driven sensory encoding, neuromorphic processing, and spiking motor control.

Distributed optimization of multi-integrator agent systems with mixed neighbor interactions

In this paper, we consider a constrained distributed optimization problem for multi-integrator agent systems with mixed neighbor interactions, where the neighbors of the agents can be classified as cooperative or noncooperative by introducing a cooperation-competition network into the modeling of the communication graph. The agent’s goal is to optimize the total objective function of the cooperators. Since agents can only communicate with their neighbors, they are unable to obtain complete information about cooperators. The distributed optimization algorithm is proposed to classify cooperative subnetworks by introducing additional auxiliary variables, and the outputs of all agents exponentially converge to the optimal solution of the optimization problem under the condition of satisfying the global equality constraint of the subnetwork. The Lyapunov stability theory is used to analyze the algorithm’s convergence. Simulation results show the effectiveness of the proposed algorithm in two examples of economic dispatch in smart grids and optimal area coverage in multi-sensor networks.

K-filter-based distributed adaptive output cluster consensus under cooperative-competitive networks

This paper investigates the K-filter-based output cluster consensus problem under cooperative-competitive networks. The agents in the network are divided into several clusters where the relationships among agents belonging to the same cluster are only cooperative while agents in different clusters have both cooperative and competitive interactions with each other. Firstly, K-filters are introduced to estimate the state variables of the system with unknown control coefficients. Then new neighborhood errors are constructed to make the couplings among agents belonging to the same cluster strong enough. Based on the K-filters and the new neighborhood errors, a distributed output-feedback control protocol is designed to achieve output cluster consensus. It is proved that all signals in the closed-loop systems are bounded and the output of each agent can track the reference signal asymptotically if the network is in-degree balanced and each cluster contains a directed spanning tree. Finally, a numerical example is provided to verify the effectiveness of the proposed control protocol.

Adaptive Bipartite Leaderless Consensus of Multiagent Systems With Uncertain MIMO Dynamics Over a Signed Directed Graph

This paper investigates the bipartite leaderless consensus problem for uncertain multi-input multi-output (MIMO) multi-agent systems (MASs) over a directed cooperation-competition network. An adaptive control protocol is proposed to achieve the bipartite consensus for agents in the presence of matching uncertainties. It is required that the signed directed graph should be structurally balanced and contain a directed spanning tree. A numerical example is performed to verify the effectiveness of the proposed algorithm.

Consensus for cooperative–competitive network systems: An impulsive perspective

AbstractUsually, hypotheses involving cooperative interactions and continuous interplay among interacting agents may result in somewhat conservatism, drastically hurdling potential about interpretations of prevalent aggregation phenomena arising in both nature and practical application. Of interest uncovered in this article is the cooperative–competitive consensus problems for multi‐agent systems by leverage of impulsive‐based control perspective. We first discuss the possibility of quantifying interaction relation of the agents and the description of cooperative–competitive phenomena that capture massive interest recently. Specifically, the former is done by algebraic graph theory, following the same research avenue as the convention; while the cooperative–competitive information is described by some nonzero scaling parameter. We shall pursue the tie that enables to anchor the consensus of participating agents, without depending on the signed graph theory. Subsequently, a manner relying on the local information is carried out to induce the consensus error to circumvent the global information. Then, consensus condition, in connection with the features of both continuous and impulsiveߚbased dynamics, is obtained, as well as several typical circumstances are also elaborated. Both the proposed consensus algorithms and the derived results are underpinned via a numerical study eventually.

Output Bipartite Consensus for Second-Order Heterogeneous Uncertain Agents With State-Dependent Cooperation-Competition Interactions

For state-dependent cooperation-competition networks, the output bipartite consensus problem of heterogeneous uncertain agents is studied in this paper. In our framework, all the agents are described by second-order continuous-time nonlinear systems with different intrinsic dynamics, and the agents' uncertainties are characterized by unknown parameters in the intrinsic term. Then, the edge evolution rules with hysteresis coefficients are proposed via the classification strategy. To solve this problem, the distributed Lyapunov-based redesign method with the potential function term is first applied to such second-order heterogeneous uncertain multi-agent system. Through the three-step correlation design, the explicit expressions of the distributed controllers and the unknown parameter estimators are obtained, and it is shown that the state-dependent cooperation-competition network is always connected and maintains structural balance for all time if an initial structurally balanced and connected network topology is provided. On this basis, it follows from the total Lyapunov function that output bipartite consensus can be achieved asymptotically for the heterogeneous uncertain multi-agent system. Finally, a numerical simulation is provided to validate the structural balance of state-dependent networks and output bipartite consensus of heterogeneous uncertain agents.

Bipartite hybrid formation tracking control for heterogeneous multi-agent systems in multi-group cooperative-competitive networks

In this paper, the problem of bipartite hybrid formation tracking for multi-agent systems with heterogeneous groups is studied. First, the network structure problem of a cluster of agents with both group relationship and cooperation-competition relationship is discussed. Then, based on the leading-following method, the system model consisting of nonlinear second-order dynamic agents and first-order linear dynamic agents is determined. In this network system, the first group of second-order dynamic individuals needs to track their leader with unknown input and realizes the bipartite time-invariant formation, while the other group tracks its leader to realize the bipartite time-varying formation. To solve this problem, a new control protocol based on neighbor information and leader dynamics is designed. In addition, for first-order dynamic individual, a velocity estimator is introduced to estimate its velocity state. Next, algebraic graph theory and Lyapunov stability theory are used to prove that the formation error can approach arbitrary neighborhood of zero. Finally, the simulation results show the feasibility of the proposed conclusions and the effectiveness of the designed velocity estimator.

P Components of Cluster-Lag Consensus for Second-Order Multiagent Systems With Adaptive Controller on Cooperative-Competitive Networks.

The consensus tracking problem means that a group of followers tracks the desired trajectory with local communication. In this article, partial components of cluster consensus have been considered. In this scenario, the p components of the followers in different clusters track the leader at different lag times, while p components of each agent in the same cluster reach a consensus, which is called p components of cluster-lag (PCCL) consensus. By using a seminorm ||xi||2,p and a Lyapunov-Krasovskii functional, PCCL consensus for second-order multiagent systems with homogeneous nonlinear systems on cooperative-competitive networks has been considered. For the case that the communication network graph is undirected, a decentralized adaptive controller, which is based on the exchanged neighbors' information from the same cluster, is designed such that all the agents reach PCCL consensus. For the directed graph case, an adaptive protocol based on the intracoupling strength is constructed for each cluster to achieve PCCL consensus. Finally, two simulation examples are illustrated to show the effectiveness of the proposed control protocols.

Nash equilibrium seeking of general linear multi-agent systems in the cooperation–competition network

Nash equilibrium seeking of general linear multi-agent systems in the cooperation–competition network

Fixed-time group consensus of second-order multi-agent systems based on event-triggered control

The problem of fixed-time group consensus for second-order multi-agent systems with disturbances is investigated. For cooperative-competitive network, two different control protocols, fixed-time group consensus and fixed-time event-triggered group consensus, are designed. It is demonstrated that there is no Zeno behavior under the designed event-triggered control. Meanwhile, it is proved that for an arbitrary initial state of the system, group consensus within the settling time could be obtained under the proposed control protocols by using matrix analysis and graph theory. Finally, a series of numerical examples are propounded to illustrate the performance of the proposed control protocol.

Leader-Following Cluster Consensus of Multiagent Systems With Measurement Noise and Weighted Cooperative–Competitive Networks

Leader-following cluster consensus is investigated for multiagent systems with weighted cooperative–competitive networks and measurement noise. A stochastic approximation protocol is proposed for interactively balanced and sub-balanced networks, and pinning control is introduced to deal with the divergence phenomenon in interactively unbalanced networks. With these protocols, sufficient conditions for reaching a strong mean-square leader-following cluster consensus are established for all the three types of networks, which are also extended to the cases without measurement noise. Numerical examples illustrate the effectiveness of the proposed protocols and theoretical analysis.

Nash Equilibrium Seeking for Incomplete Cluster Game in the Cooperation–Competition Network

In this article, we investigate the problem of seeking Nash equilibrium (NE) in multiagent systems within cooperation–competition networks. Each agent aims to optimize a total cost function that accounts for its own interests as well as those of its cooperators, considering both cooperative and noncooperative interactions with other agents. It is worth noting that, unlike in existing N-coalition games, the agents in this study only have knowledge of whether they are cooperative or noncooperative with their neighboring agents; they do not have information about non-neighboring agents who might be cooperators. As a result, due to potential disconnections in the communication topology within the cluster, it is not possible to consider the entire cluster as a virtual player to optimize its objective functions. To address this issue, we developed an algorithm using the singular perturbation technique, which divides the system into two distinct timescales. We propose a novel estimation algorithm to estimate the total cost function of disconnected subnetworks within the fast system. In the slow system, the search for NE is based on a gradient algorithm, while the Lyapunov stability theory is utilized to analyze the convergence of the algorithms. Furthermore, we extend the problem to accommodate scenarios where multiple subnetworks exist within the network. Numerical simulations are conducted to demonstrate their capability for resolving the noncooperative game problem in cooperation–competition networks.

Complex group consensus of multi-agent systems with cooperative-competitive mechanisms: acyclic partition method

For multi-agent system(MAS) with cooperative-competitive mechanisms, we aim to investigate its complex group consensus such as group scaled consensus and group-bipartite scaled consensus. Firstly, for directed graph G with m closed strongly connected components (CSCCs), we show the existence and nonuniqueness of its acyclic partition by giving a CSCC-based acyclic partition strategy, where MAS is divided into m subnetworks with directed spanning trees and the condensed graph of G is a directed acyclic graph. Secondly, under the extensive in-degree balance condition for cooperative-competitive network, an appropriate distributed control protocol is designed to guarantee that MASs can achieve group/group-bipartite scaled consensus by introducing cooperative-competitive matrix and scaling parameter matrix. Furthermore, to guarantee the faster convergence of MASs, we further improve and optimize the acyclic partition strategy by rich algebra and graph theories in three aspects: the selection of sub-root nodes, the division of multi-reachable nodes and the selection of directed spanning tree in each subnetwork. Finally, some simulation examples are presented to verify the effectiveness of our results.

A Distributed Lyapunov-Based Redesign Approach for Heterogeneous Uncertain Agents With Cooperation-Competition Interactions.

A swarming behavior problem is investigated in this article for heterogeneous uncertain agents with cooperation-competition interactions. In such a problem, the agents are described by second-order continuous systems with different intrinsic nonlinear terms, which satisfies the "linearity-in-parameters" condition, and the agents' models are coupled together through a distributed protocol containing the information of competitive neighbors. Then, for four different types of cooperation-competition networks, a distributed Lyapunov-based redesign approach is proposed for the heterogeneous uncertain agents, where the distributed controller and the estimation laws of unknown parameters are obtained. Under their joint actions, the heterogeneous uncertain multiagent system can achieve distributed stabilization for structurally unbalanced networks and output bipartite consensus for structurally balanced networks. In particular, the concept of coherent networks is proposed for structurally unbalanced directed networks, which is beneficial to the design of distributed controllers. Finally, four illustrative examples are given to show the effectiveness of the designed distributed controller.

Group consensus of heterogeneous multi-agent systems with packet loss and unknown speed of second-order agents in cooperative–competitive networks

Group consensus of heterogeneous multi-agent systems with packet loss and unknown speed of second-order agents in cooperative–competitive networks