Homogeneous Multi-agent Systems Research Articles

Scale-Free Noncollaborative Linear Protocol Design for a Class of Homogeneous Multi-Agent Systems

Scale-Free Noncollaborative Linear Protocol Design for a Class of Homogeneous Multi-Agent Systems

Preface

Hosted by Nanjing Tech University, China, the 2023 2nd International Conference on Aerospace and Control Engineering (ICoACE 2023) was held from 15th to 17th December 2023 in Nanjing, China. With ICoACE 2023, the conference series International Conference on Aerospace and Control Engineering (ICoACE) completed its second edition.The general topics of ICoACE 2023 fall into mainly four categories: 1) Aerospace Materials; 2) Aerospace Science and Technology; 3) Control Engineering; 4) Aerospace and Mechanics. Apart from the general topics, ICoACE 2023 attracted a number of papers related to aerospace and control engineering. Topics at the Conference include but are not limited to: Materials Failure Analysis and Prediction Prevention, Spacecraft Thermal Control Technology, Aerospace Computing, Combustion and Energy Conversion, Electric Automation, Drive Motor and Control Technology, etc.All papers were exhaustively reviewed by program committee members and peer-reviewers, who took into account the breadth and depth of the research topics that fall under the scope of ICoACE. The most promising and ICoACE mainstream-relevant contributions were selected from all the submissions for presentation and inclusion in this volume, which presents innovative original ideas or results of general significance supported by clear and rigorous reasoning and compelling new evidence, as well as methods.The Conference included three days of technical parts consisting mainly of keynote speeches, oral presentations, poster presentations and academic discussion. We were delighted and honored to invite Prof. Yan Wang (Nanjing University of Aeronautics and Astronautics, China), Prof. Mouquan Shen (Nanjing Tech University, China), and Prof. Wenfeng Hu (Central South University, China) to share their insightful ideas and mind-blowing researches as keynote speakers. Based on the research The Cooperative Control of Multi-Agent Systems with High Comprehensive Performance, Prof. Wenfeng Hu first introduced the event-triggered control strategies for multi-agent systems to reduce the communication burden among agents and talked about the brought challenging issues. Then, he addressed the consensus of homogeneous multi-agent systems and cooperative output regulation of heterogeneous multi-agent systems with event-triggering communication. Moreover, he presented the developed reset control strategies for multi-agent systems with second-order dynamics. Finally, an outlook on the new topics and trend for the cooperative control of networked control systems theory and techniques were given.We would like to thank all the keynote speakers, authors, program committee members and anonymous reviewers for their efforts in making ICoACE 2023 a Conference of the highest standard. We are also grateful to Journal of Physics: Conference Series, for its help in publishing this Proceedings of the Conference.The Committee of ICoACE 2023List of Committee Member is available in this pdf.

Group-aggregation of Hierarchical Containment Control for Homogeneous Multi-agent Systems in Precision Agriculture

Group-aggregation of Hierarchical Containment Control for Homogeneous Multi-agent Systems in Precision Agriculture

Bumpless transfer consensus control for linear multi-agent systems under agent-dependent switching directed topologies

The bumpless transfer consensus control problem for linear homogeneous multi-agent systems subject to agent-dependent switching directed communication topologies is investigated in this paper. According to the information of graph Laplacian matrix and relative state of agent, we propose a novel agent-dependent switching law to guide the switching among these topologies, then the leader-following consensus performance of multi-agent systems is achieved via using the multiple Lyapunov functions method. Further, since the switching consensus controllers are considered, the control signal bumps are inevitable at switching instants, and some sufficient conditions via linear matrix inequalities are developed to pursue the bumpless transfer performance of multi-agent systems. Besides, the agent-dependent switching law also allows the error dynamics under each directed subtopology to be unstabilizable. Finally, the theoretical results are verified by two illustrative examples.

Dynamic event‐triggered control for the leader‐following consensus of multi‐agent systems with input time delay

SummaryIn this article, the leader‐following consensus of a kind of linear homogeneous MASs (multi‐agent systems) with input time delay is investigated. To realize leader‐following consensus, a novel distributive input control protocol based on dynamic event‐triggered mechanism is proposed. In this novel protocol, internal dynamic variables are employed to reduce the burden of communication and exclude the Zeno phenomenon. By utilizing the graph algebra, stability analysis and Lyapunov theory, some sufficient conditions have been obtained for the consensus of this MASs. The effectiveness of our approach is illustrated by some simulation examples. Compared with the given achievements, the obtained results have better ability in accelerating convergence rate and reducing the burden of communication.

Event-Triggered Distributed Data-Driven Iterative Learning Bipartite Formation Control for Unknown Nonlinear Multiagent Systems.

In this study, we investigate the event-triggering time-varying trajectory bipartite formation tracking problem for a class of unknown nonaffine nonlinear discrete-time multiagent systems (MASs). We first obtain an equivalent linear data model with a dynamic parameter of each agent by employing the pseudo-partial-derivative technique. Then, we propose an event-triggered distributed model-free adaptive iterative learning bipartite formation control scheme by using the input/output data of MASs without employing either the plant structure or any knowledge of the dynamics. To improve the flexibility and network communication resource utilization, we construct an observer-based event-triggering mechanism with a dead-zone operator. Furthermore, we rigorously prove the convergence of the proposed algorithm, where each agent's time-varying trajectory bipartite formation tracking error is reduced to a small range around zero. Finally, four simulation studies further validate the designed control approach's effectiveness, demonstrating that the proposed scheme is also suitable for the homogeneous MASs to achieve time-varying trajectory bipartite formation tracking.

Observer-based leader-following consensus for positive multi-agent systems over time-varying graphs

This paper addresses the leader-following consensus problem for discrete-time positive multi-agent systems over time-varying graphs. We assume that the followers may have mutually different positive dynamics that can also be different from the leader. Compared with most existing positive consensus works for homogeneous multi-agent systems, the formulated problem is more general and challenging due to the interplay between the positivity requirement and high-order heterogeneous dynamics. To solve the problem, we present an extended version of existing observer-based design for positive multi-agent systems. By virtue of the common quadratic Lyapunov function technique, we show the followers will maintain their state variables in the positive orthant and finally achieve an output consensus specified by the leader. A numerical example is used to verify the efficacy of our algorithms.

Learning a data-efficient model for a single agent in homogeneous multi-agent systems

Learning a data-efficient model for a single agent in homogeneous multi-agent systems

Distributed Adaptive Event-Triggered TVGF for Generic Linear Multi-Agent Systems Under Directed Graph

This paper investigates event-triggered time-varying grouping formation (TVGF) problem for generic linear homogeneous multi-agent systems under directed graphs. A distributed adaptive event-triggered TVGF algorithm is proposed to ensure that the goal of TVGF can be achieved and the communication frequency between agents is reduced. Compared with previous formation control methods, the algorithm presented in this paper can complete the group formation task while saving communication energy. In addition, the algorithm’s dependence on global network information is also eliminated by using adaptive techniques. Finally, the availability of the proposed control law is established by numerical simulation.

Aperiodic sampled‐data consensus control for homogeneous and heterogeneous multi‐agent systems: A looped‐functional method

AbstractThis paper examines the aperiodic sampled‐data consensus control problems for nonlinear homogeneous and heterogeneous multi‐agent systems (MASs). For homogeneous MASs, the consensus conditions, with and without time delays are derived by using a two‐sided‐looped functional. This less conservative approach takes full account of the information on the intervals to and to . On this basis, two aperiodic sampled‐data controllers are designed, and additional results on the estimate for the upper bound of the maximum allowable sampling interval are given as well. For heterogeneous MASs, the bounded consensus tracking criterion is derived based on the improved looped functional method. In addition, a corresponding heterogeneous sampled‐data controller is designed for each follower to ensure that the consensus tracking error exponentially converges to a bounded ellipsoid region. Finally, three illustrative examples are provided to show the efficiency and superiority of the proposed results.

On Estimation of Reachable Sets for Approximate Consensus of Homogeneous Multiagent Systems Subject to Bounded Exogenous Disturbances

This paper is concerned with the reachable set estimation for leaderless discrete-time homogeneous multiagent systems (MASs) with general linear high-order dynamics and bounded exogenous disturbances. The interagent interactions are described by undirected connected graphs, where fixed and randomly switching manners are considered. To govern the evolution of the randomly switching, a Markov chain is introduced with consideration of completely and partially known transition probabilities. By collecting in-neighbors' information, the protocol is designed to ensure certain properties, including (I) the state trajectories of all agents are constrained within a compact set; (II) the noise-unperturbed MAS achieves asymptotic consensus in the exponential stability sense; (III) the approximate consistency is regulated for the noise-perturbed MAS in the input-to-state stability sense. Thanks to the constructed nonsingular transformation, one explicitly characterizes the agreement dynamics by the consensus function with determined initial condition. It is deduced that the intersection of estimated reachable sets of the concerned MAS and the agreement dynamics is nonempty. Numerical simulations confirm the theoretical analysis.

Robust Performance Analysis for Time-Varying Multi-Agent Systems with Stochastic Packet Loss

Recently, a scalable approach to system analysis and controller synthesis for homogeneous multi-agent systems with Bernoulli distributed packet loss has been proposed. As a key result of that line of work, it was shown how to obtain upper bounds on the H2-norm that are robust with respect to uncertain interconnection topologies. The main contribution of the current paper is to show that the same upper bounds hold not only for uncertain but also time-varying topologies that are superimposed on the stochastic packet loss. Because the results are formulated in terms of linear matrix inequalities that are independent of the number of agents, multi-agent systems of any size can be analysed efficiently. The applicability of the approach is demonstrated on a numerical first-order consensus example, on which the obtained upper bounds are compared to estimates from Monte-Carlo simulations.

Using Fuzzy Logic to Learn Abstract Policies in Large-Scale Multiagent Reinforcement Learning

Large-scale multiagent reinforcement learning requires huge computation and space costs, and the too-long execution process makes it hard to train policies for agents. This work proposes a concept of fuzzy agent, which is a new paradigm for training homogeneous agents. Aiming at a lightweight and affordable reinforcement learning mechanism for large-scale homogeneous multiagent systems, we break the one-to-one correspondence between agent and policy, designing abstract agents as the substitute for the multiagent to interact with the environment. The Markov decision process models for these abstract agents are conducted by fuzzy logic, which also acts on the behavior mapping from abstract agent to entity. Specifically, just the abstract agents execute their policy at a time step, and the concrete behaviors are generated by simple matrix operations. The proposal has lower space and computation complexities because the number of abstract agents is far less than that of entities, and the coupling among agents is retained implicitly. Compared with other approximation and simplification methods, the proposed fuzzy agent not only greatly reduces required computing resources but also ensures the effectiveness of the learned policies. Several experiments are conducted to validate our method. The results show that the proposal outperforms the baseline methods, while it has satisfactory zero-shot and few-shot transfer abilities.

Neuro-adaptive cooperative control for a class of high-order nonlinear multi-agent systems

In this paper, we studied the cooperative control problem for a class of high-order nonlinear multi-agent systems (MASs) with external disturbance and system uncertainty. A neuro-adaptive robust controller with sliding mode variable structure method, with an online-learning RBF-like neural network was proposed to approximate the nonlinear terms. Further, sliding mode variable structure method was used to eliminate the influence of external disturbance and system uncertainty. Lyapunov stability theorem verified the capability of system consensus, and the sufficient conditions for cooperatively uniformly ultimately bounded (CUUB) are also given. At last, two numerical simulations on both homogeneous and heterogeneous MASs demonstrated the effectiveness of our proposed method.

Stability analysis and parameter coordination of the grid-connected wind farm with heterogeneous PMSGs

Improper setting of phase-locked loop (PLL) control parameters is one of the key factors for the adverse dynamic interaction between wind turbine generators, resulting in the oscillation instability of wind power systems. In this study, considering the spatial distribution characteristics and dynamic heterogeneity of multiple PMSGs, the PLL-induced instability mechanism of the grid-connected PMSG wind farm is studied and a stability criterion is proposed to guide the parameter coordination of the PLL for multiple PMSGs. First, based on the dynamic equivalent modeling method of the homogeneous multi-agent system, a reduced-order model of the homogeneous wind farm dominated by PLL is proposed. The parameter region of PLL control to ensure stability was analytically examined based on the proposed stability criterion. Then, the stability of heterogeneous wind farm is analyzed by constructing an equivalent homogeneous wind farm that has the same dominant oscillation mode as the heterogeneous wind farm; that is, the stability is equivalent. Furthermore, the stability criterion of the heterogeneous wind farm is deduced, and the parameter coordination method of PLL control for multiple PMSGs is proposed. Modal analysis and nonlinear simulation were conducted to confirm the accuracy of the theoretical analysis.

Global Leader-Following Consensus of Double-Integrator Multiagent Systems by Fully Distributed Bounded Linear Protocols

This article is concerned with the global leader-following consensus problem of the input-constrained double-integrator multiagent systems. By utilizing a Lyapunov-like function consisting of a positive semidefinite term and an integral term, a class of bounded static linear protocols are proposed to ensure that the global leader-following consensus problem for homogeneous multiagent systems is solved in a fully distributed manner for all undirected communication graphs. As a further result, the global consensus problem for heterogeneous multiagent systems under directed communication graphs is also considered and two different distributed observer-based bounded linear protocols are proposed to solve such a problem also in a fully distributed manner. The most significant advantages of this article are that the system under consideration is more general, the designed protocols are linear, and the global consensus in a fully distributed manner can be guaranteed. A numerical example shows the effectiveness of the proposed approaches.

H ∞ Consensus Design and Online Scheduling for Multiagent Systems with Switching Topologies via Deep Reinforcement Learning

This paper is devoted to H ∞ consensus design and online scheduling for homogeneous multiagent systems (MASs) with switching topologies via deep reinforcement learning. The model of homogeneous MASs with switching topologies is established based on switched systems theory, in which the switching of topologies is viewed as the switching among subsystems. By employing linear transformation, the closed-loop systems of MASs are converted into reduced-order systems. The problem of H ∞ consensus design can be transformed to the issue of H ∞ control. It is supposed that the consensus protocol is composed of two parts: dynamics-based protocol and learning-based protocol, where dynamics-based protocol is provided to guarantee the convergence and weighted attenuation and learning-based protocol is proposed to improve the transient performance. Then, the multiple Lyapunov function (MLF) method and mode-dependent average dwell time (MDADT) method are combined to ensure the stability and weighted H ∞ disturbance attenuation index of reduced-order systems. The sufficient existing conditions of dynamics-based protocol are given through the feasible solutions of linear matrix inequalities (LMIs). Moreover, the online scheduling is formulated as a Markov decision process, and the deep deterministic policy gradient (DDPG) algorithm in the framework of actor-critic is proposed for the compensation of disturbance to explore optimal control policy. The online scheduling of parameters of MASs is viewed as bounded compensation of dynamics-based protocol, whose stability can be guaranteed by nonfragile control theory. Finally, simulation results are provided to illustrate the effectiveness and superiority of the proposed method.

On linear-quadratic optimal transition in synchronization of homogeneous multi-agent systems

Abstract In this paper, a new approach for the optimal synchronization of homogeneous multi-agent systems is presented. In contrast to existing approaches, we offer an intuitive cost for the transition of each agent to a common synchronization trajectory, which facilitates tuning of the cost weights. The introduced LQ optimal tracking problem is solved by reducing it to a LQ regulator problem. By choosing a linear parameterization of the synchronization trajectory, we enable the distributed implementation of the optimal synchronization controller. We give examples how such a parameterization can be chosen and how it influences the required communication between the agents. Furthermore, we derive an optimal communication topology that minimizes the weighted sum of all agents’ transition costs. Finally, we demonstrate our results in a simulation example.

LMI-based consensus of linear multi-agent systems by reduced-order dynamic output feedback

This work proposes new conditions for consensus of homogeneous multi-agent systems subjected to exogenous disturbances in directed communication graphs by dynamic output feedback protocols. The agents under investigation are described as linear dynamics, and the communication network is such that each agent receives as information only the output of neighbor agents. The synchronization problem is rewritten as an output feedback stabilization without requiring the Laplacian matrix to be diagonalizable. As the main appeal, we propose new necessary and sufficient conditions for the design of dynamic output feedback controllers of arbitrary order – including static output feedback as a particular case – and sufficient Linear Matrix Inequalities (LMIs) for H∞ consensus. Numerical experiments illustrate the effectiveness of the proposed approach.

Event/Self-Triggered Approximate Leader-Follower Consensus With Resilience to Byzantine Adversaries

Distributed event- and self-triggered controllers are developed for approximate leader-follower consensus with robustness to adversarial Byzantine agents for a class of homogeneous multi-agent systems (MASs). A strategy is developed for each agent to detect Byzantine agent behaviors within their neighbor set and then selectively disregard their transmission. Selectively removing Byzantine agents results in time-varying discontinuous changes to the network topology. Nonsmooth dynamics also result from the use of event/self-triggered strategies and triggering condition estimators that enable intermittent communication. Nonsmooth Lyapunov methods are used to prove approximate consensus of the MAS consisting of the remaining cooperative agents. Simulations are included to validate the result and to outline the tradeoff between communication and performance.