Heterogeneous Multiagent Networks Research Articles

Stochastic Consensus for Heterogeneous Multi-agent Networks With Constraints and Communication Noises

Stochastic Consensus for Heterogeneous Multi-agent Networks With Constraints and Communication Noises

Consensus of Heterogeneous Multi-agent Networks Under Sparse Attacks by Integral Type Observer

Consensus of Heterogeneous Multi-agent Networks Under Sparse Attacks by Integral Type Observer

Controllability of consensus heterogeneous multi-agent networks over continuous time scale

Abstract The research, presented in this paper, concernes the controllability of a multi-agent network with a directed, unweighted, cooperative, and time-invariant communication topology. The network’s agents follow linear and heterogeneous dynamics, encompassing first-order, second-order, and third-order differential equations over continuous time. Two classes of neighbour-based linear distributed control protocols are considered: the first one utilises average feedback from relative velocities/relative accelerations, and the second one utilises feedback from absolute velocities/absolute accelerations. Under both protocols, the network’s agents achieve consensus in their states asymptotically. We observe that both of the considered dynamical rules exploit the random-walk normalised Laplacian matrix of the network’s graph. By categorising the agents of the network into leaders and followers, with leaders serving as exogenous control inputs, we analyse the controllability of followers within their state space through the influence of leaders. Specifically, matrix-rank conditions are established to evaluate the leader–follower controllability of the network under both control protocols. These matrix-rank conditions are further refined in terms of the system matrices’ eigenvalues and eigenvectors. The inference diagrams presented in this work provide deeper insights into how leader–follower interactions impact the network controllability. The efficacy of the theoretical findings is validated through numerical examples.

Distributed discrete-time optimization of heterogeneous multi-agent networks with unbounded position constraints and nonconvex velocity constraints

The distributed discrete-time heterogeneous multi-agent optimization problem with unbounded position constraints and nonconvex velocity constraints was considered in this paper, where the models of agents are described by first- or second- order difference equations, the position constraints are assumed to be unbounded and convex and the velocity constraints are assumed to be bounded, closed and nonconvex. An improved distributed algorithm is adopted, under which, all agents could collaboratively minimize the entire objective function and the positions and velocities could always stay at the corresponding constraint sets. Then the convergence analysis is completed by the coordination transformation and the properties of projection operator and constraint operator. Finally, we give examples to illustrate the correctness of results.

Constrained Consensus of Continuous-Time Heterogeneous Multi-Agent Networks with Nonconvex Constraints and Delays

Constrained Consensus of Continuous-Time Heterogeneous Multi-Agent Networks with Nonconvex Constraints and Delays

Output Homogenization and Synchronization of Heterogeneous Nonlinear Multiagent Networks

In this article, we are concerned with the output synchronization of a group of heterogeneous agents with nonlinear dynamics. It is shown that via dynamic coupling, the output synchronization problem is transformed into a family of output homogenization and internal-model-based synchronization problems. In contrast to conventional approaches, the adopted approach does not rely on the generation of a specific reference trajectory, to be tracked by all agents. Based on this premise, we propose a distributed state-feedback protocol driving all the heterogeneous multiagent individual systems onto a copy of a virtual reference signal. Invoking the results from the theory of output regulation and cooperative control, the state-feedback controller synthesis is conducted to achieve the output synchronization of heterogeneous multiagent networks. Immediate extensions of the obtained results to observer-based control are also discussed. The effectiveness of the proposed output synchronization algorithm is verified through theoretical analysis and case studies.

Distributed Heterogeneous Multi-Agent Networks Optimization with Nonconvex Velocity Constraints

This paper briefly deals with the distributed optimization problem over heterogeneous multi-agent networks with nonconvex velocity constraints. Based on the constraint operator, a distributed optimization algorithm is proposed for each agent, where the gradient gains are nonuniform and only local neighbors’ information is used by each agent. Then, a time-varying coordination transformation is introduced to change the closed-loop system into a new system, which is composed of linear part and nonlinear part. It is proved that the system matrices of linear part are time-varying stochastic matrices and the system states are bounded. Furthermore, under some mild assumptions, it is shown that all agents’ position states could converge to the optimal solution of the team objective function, while the velocities of all agents are constrained to the corresponding nonconvex sets. Finally, the feasibility of the proposed algorithm is verified by simulations.

Workspace Partitioning and Topology Discovery Algorithms for Heterogeneous Multiagent Networks

In this paper, we consider a class of workspace partitioning problems that arise in the context of area coverage and spatial load balancing for spatially distributed heterogeneous multi-agent networks. It is assumed that each agent has certain directions of motion or directions for sensing and exploration that are more preferable than others. These preferences are measured by means of convex and anisotropic (direction-dependent) quadratic proximity metrics which are, in general, different for each agent. These proximity metrics induce Voronoi-like partitions of the network's workspace that are comprised of cells which may not always be convex (or even connected) sets but are necessarily contained in ellipsoids that are known to their corresponding agents. The main contributions of this work are 1) a distributed algorithm for the computation of a Voronoi-like partition of the workspace of a heterogeneous multi-agent network and 2) a systematic process to discover the network topology induced by the latter Voronoi-like partition. Numerical simulations that illustrate the efficacy of the proposed algorithms are also presented.

Cluster Tracking Performance Analysis of Linear Heterogeneous Multi-Agent Networks: A Complex Frequency Domain Approach

This paper presents a complex frequency domain approach to investigate the cluster tracking performance of linear heterogeneous multi-agent networks. Firstly, a designed graph matrix is given to eliminate the interaction influence between coupled subnetworks with different tracking signals. On this basis, the cluster tracking performance analysis problem is decomposed into two parts, i.e., stability analysis and steady-state performance analysis of multi-agent networks. Secondly, based on the network structure, a minimum separable subgraph set is proposed to transform the stability of the multi-agent network into the stability of the separable subnetworks. Further, a signal flow diagram is introduced to solve the stability of the separable subnetworks. Thirdly, in view of Final-value theorem, a necessary and sufficient condition is proposed for achieving the cluster tracking control for the closed-loop stable multi-agent network. This result provides a strong theoretical basis for the flexible design of controllers due to that it quantitatively reveals the inherent law of steady-state performance for multi-agent networks. Finally, an algorithm is established to calculate the steady-state error of a general directed multi-agent network. Some simulations are provided to demonstrate the effectiveness of the theoretical results.

Group consensus for a class of heterogeneous multi-agent networks in the competition systems

In this paper, we investigate the group consensus issues of a continuous-time heterogeneous multi-agent networks. The system consists of two types of agents, the first-order and second-order, respectively. Be distinct from the related works, we propose a new distributed group consensus protocol which modeled by the agents’ competition interaction. Based on stability theory and the method of frequency domain, the systems’ group consensus including these two cases are discussed: without and with the influence of time delays. Meanwhile, the corresponding criteria are theoretically obtained. Specially, these criteria we obtained do not rely on the conservative conditions which exist in the relevant research works, such as in-degree balance and so on. Furthermore, we also theoretically establish the tolerable upper bounds of the time delays for achieving group consensus. The validity of our findings is verified through some numerical simulation examples.

Group Consensus for Heterogeneous Multiagent Systems in the Competition Networks With Input Time Delays

The group consensus problems of heterogeneous multiagent networks with input time delays are investigated in this paper. In this complex networks, the agents' dynamics are modeled by the first-order and the second-order multiagent systems, where a novel dynamic group consensus protocol is designed through the competitive relationship among the agents. By using matrix theory and the frequency-domain method, some algebraic criteria are theoretically established for reaching a group consensus in the following two cases: 1) with the identical and 2) different input time delays. Meanwhile, the conservative assumptions existed in the relevant literatures are absolutely relaxed, i.e., the in-degree balance and the geometric multiplicity of zero eigenvalue of Laplacian matrix are at least two. Finally, the effectiveness of our results is illustrated by numerical examples.

Event-Triggered Output Regulation of Heterogeneous Multiagent Networks

This technical note addresses the event-triggered output regulation problem of heterogenous multiagent systems. In contrast to similar problems solved by algorithms based on continuous communication between neighboring agents, a distinctive feature of the proposed algorithm is that communication from one agent to another is mediated by the corresponding triggering condition of the link. The output regulation problem is solved by a distributed design of predictors, observers, detectors, and controllers. In addition, positive lower bounds of interevent times for all links are provided. The effectiveness of the proposed algorithm is demonstrated by a numerical example.

Cooperative Output Regulation of Heterogeneous Linear Multi-Agent Networks via <formula> <tex>${H}_{\infty}$</tex> </formula> Performance Allocation

The cooperative output regulation (COR) problem of heterogeneous linear multi-agent networks [multi-agent systems (MASs)] is studied in the paper. Agents are allowed to be inherently heterogeneous linear time-invariant systems and the communication graph is not restricted to be acyclic. Both state- and output-feedback control protocols that make use of neighbors' output information are considered. In light of robust control theory and robust output regulation theory, small-gain solvability conditions with closed-loop pole specifications are first derived. Then, an H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> performance allocation approach is proposed for protocol design, where algorithms for optimizing graph weights and designing protocol gains are constructed, respectively. Both continuous-time (CT) and discrete-time MASs are investigated in a unified framework. In addition, solvability of the COR problem is further discussed from the graph, agent, and exosystem aspects, respectively. It is proved that the proposed approach pre-explicitly ensures the solvability of the COR problem for either of the three cases: the graph is acyclic; for CT MASs, agents are minimum-phase and right-invertible; and for CT MASs, the poles of the exosystem have zero real part and agents are right-invertible.

Fault Detection and Isolation in Linear Heterogeneous Multi-Agent Networks

Detection and isolation of faulty behaviours in multi-agent dynamical networks under cooperative control laws is of paramount importance to secure that the network operation is not jeopardized by the failing of one or more of its nodes. The paper investigates under which conditions the fault detectability and isolability (FDI) properties of the individual agent are improved because of the information sharing with other agents in the network. Relying on the parity space method and focusing the analysis on linear heterogeneous dynamical systems two main contributions are achieved: (i) the sharing of information through the cooperative control law does not improve the FDI properties of the individual agents; (ii) the presence of relative measurements among the agents results in improved fault detection and isolation capabilities the multi-agent system under the assumption of a strongly connected network.

Heterogeneous and Competitive Multiagent Networks: Couple-Group Consensus with Communication or Input Time Delays

This paper discusses the couple-group consensus problems for a class of heterogeneous multiagent networks including the following two cases: with communication and input time delays, respectively. Different from the related cooperative networks, two novel delayed group consensus protocols are designed based on the competitive relationship between the agents. Furthermore, we absolutely relax the in-degree balance and other restrictive preconditions which existed in the relevant works. Some sufficient algebraic criteria for the achievement of couple-group consensus and the upper bound of the input time delays are technically obtained via the frequency domain method and matrix theory, respectively. The results show that the achievement of the couple-group consensus depends on the second-order agents’ in-degree and the control parameters of the systems, whereas it is independent of the communication time delays. Meanwhile, the upper bound of the input time delay is determined by the control parameters and the in-degree of the first-order agents. Finally, the validity of the proposed results is verified by several simulated examples.

Consensus tracking in heterogeneous nonlinear multi-agent networks with asynchronous sampled-data communication

This paper is concerned with the problem of consensus tracking for heterogeneous nonlinear multi-agent systems via asynchronous sampled-data information. Due to the existence of heterogeneity of dynamics and asynchrony of sampled information, the closed-loop error tracking system with ‘disturbance-like’ term can be established. By utilizing the Lyapunov–Krasovskii approach, the bounded consensus condition with the estimate of the tracking error bound is derived. Based on the criterion, an effective method for designing the appropriate consensus controller gain matrices is developed. An illustrative example with the coupled pendulums is given to demonstrate the efficiency of the results obtained in this paper.

Pattern Synchronization of Nonlinear Heterogeneous Multiagent Networks With Jointly Connected Topologies

In this paper, a decentralized control algorithm is proposed for a group of heterogeneous agents with nonlinear dynamics such that it achieves synchronization in diverse collective motion patterns. With the aid of a group of deliberately designed linear homogeneous reference models, the synchronization problem is converted into a class of regulation problem. The regulation problem is hence solved using the nonlinear output feedback control technique. The conversion and, hence, the decentralized synchronization algorithm are valid when the multiagent system is jointly connected in terms of its time-varying network topologies. The effectiveness of the algorithm is verified through theoretical analysis.

Partial difference equation based model reference control of a multiagent network of underactuated aquatic vehicles with strongly nonlinear dynamics

In a recent work, the authors presented an extension of robust model reference adaptive control (MRAC) laws for spatially varying partial differential equations (PDEs) proposed by them earlier for the decentralized adaptive control of heterogeneous multiagent networks with agent parameter uncertainty using the partial difference equations (PdEs) on graphs framework. The examples provided demonstrated the capabilities of this approach under the assumption that individual vehicles executing coordinated maneuvers were fully actuated and characterized by linear dynamics. However, detailed models for autonomous vehicles–whether terrestrial, aerial, or aquatic–are often underactuated and strongly nonlinear. Using this approach, but assuming the plant parameters to be known, this work presents the model reference (MR) control laws without adaptation for the coordination of underactuated aquatic vehicles modeled individually in terms of strongly nonlinear dynamic equations arising from ideal planar hydrodynamics. The case of unknown plant parameters for this class of underactuated agents with complex dynamics is an open problem. The paper is based on an invited talk on adaptive control presented at the 2008 World Congress of Nonlinear Analysts.

Disturbance rejection in robust PdE-based MRAC laws for uncertain heterogeneous multiagent networks under boundary reference

Disturbance is a pervasive source of uncertainty in most applications. This paper presents model reference adaptive control (MRAC) laws for uncertain multiagent networks with a disturbance rejection capability. The algorithms proposed can also be viewed as the extension of the robust model reference adaptive control (MRAC) laws with disturbance rejection recently derived for systems described by parabolic and hyperbolic partial differential equations (PDEs) with spatially-varying parameters under distributed sensing and actuation to heterogeneous multiagent networks characterized by parameter uncertainty. The latter extension is carried out using partial difference equations (PdEs) on graphs that preserve parabolic and hyperbolic like cumulative network behavior. Unlike in the PDE case, only boundary input is specified for the reference model. The algorithms proposed directly incorporate this boundary reference input into the reference PdE to generate the distributed admissible reference evolution profile followed by the agents. The agent evolution thus depends only on the interaction with the adjacent agents, making the system fully decentralized. Numerical examples are presented as well. The resulting PdE MRAC laws inherit the robust linear structure of their PDE counterparts.

PdE-based model reference adaptive control of uncertain heterogeneous multiagent networks

Robust globally stable model reference adaptive control (MRAC) laws recently derived for systems described by parabolic and hyperbolic partial differential equations (PDEs) with spatially-varying coefficients under distributed sensing and actuation are extended to heterogeneous multiagent networks characterized by parameter uncertainty. The extension is carried out using partial difference equations (PdEs) on graphs that preserve parabolic- and hyperbolic-like cumulative network behavior. Unlike in the PDE case, only boundary input is specified for the reference model. The algorithms proposed directly incorporate this boundary reference input into the reference PdE to generate the distributed admissible reference evolution profile followed by the agents. The agent evolution thus depends only on the interaction with the adjacent agents, making the system fully decentralized. Numerical examples are presented as well, including the case of the switched topology associated with a sudden loss of an agent. The resulting PdE MRAC laws inherit the robust linear structure of their PDE counterparts.