Cooperative Control Of Multi-agent Systems Research Articles

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.

Cooperative Control of Multiagent Systems: A Quantization Feedback-Based Event-Triggered Approach.

This article addresses the synchronization tracking problem for high-order uncertain nonlinear multiagent systems via intermittent feedback under a directed graph. By resorting to a novel storer-based triggering transmission strategy in the state channels, we propose an event-triggered neuroadaptive control method with quantitative state feedback that exhibits several salient features: 1) avoiding continuous control updates by making the parameter estimations updated intermittently at the trigger instants; 2) resulting in lower-frequency triggering transmissions by using one event detector to monitor the triggering condition such that each agent only needs to broadcast information at its own trigger times; and 3) saving communication and computation resources by designing the intermittent updating of neural network weights using a dual-phase technique during the triggering period. Besides, it is shown that the proposed scheme is capable of steering the tracking/disagreement errors into an adjustable neighborhood close to the origin, and the existence of a strictly positive dwell time is proved to circumvent Zeno behavior. Both theoretical analysis and numerical simulation authenticate and validate the efficiency of the proposed protocols.

Review on Cooperative Control of Multi-Agent Systems

Review on Cooperative Control of Multi-Agent Systems

Adaptive Fuzzy Prescribed Performance Output-Feedback Cooperative Control for Uncertain Nonlinear Multiagent Systems

The paper considers the prescribed performance output-feedback cooperative control of multi-agent systems with nonparametric uncertainties. Scale and performance functions are proposed to construct a barrier function, based on which the adaptive prescribed performance control scheme is devel- oped. Therein, the fuzzy observers are employed to estimate the unavailable states. The proposed scheme has the following characteristics: 1) the tracking errors are always limited to a specified boundary. 2) the tracking error converges to a specified accuracy within a given time, and the settling time and tracking accuracy are determined only by the user and no longer depend on the initial conditions. 3) the distributed fuzzy controller design uses only the output information from agent and its neighbor. The effectiveness and practicality of the proposed method are demonstrated by two simulation examples.

Suboptimal Leader-to-Coordination Control for Nonlinear Systems With Switching Topologies: A Learning-Based Method.

In the cooperative control for multiagent systems (MASs), the key issues of distributed interaction, nonlinear characteristics, and optimization should be considered simultaneously, which, however, remain intractable theoretically even to this day. Considering these factors, this article investigates leader-to-formation control and optimization for nonlinear MASs using a learning-based method. Under time-varying switching topology, a fully distributed state observer based on neural networks is designed to reconstruct the dynamics and the state trajectory of the leader signal with arbitrary precision under jointly connected topology assumption. Benefitted from the observers, formation for MASs under switching topologies is transformed into tracking control for each subsystem with continuous state generated by the observers. An augmented system with discounted infinite LQR performance index is considered to optimize the control effect. Due to the complexity of solving the Hamilton-Jacobi-Bellman equation, the optimal value function is approximated by a critic network via the integral reinforcement learning method without the knowledge of drift dynamics. Meanwhile, an actor network is also presented to assure stability. The tracking errors and estimation weighted matrices are proven to be uniformly ultimately bounded. Finally, two illustrative examples are given to show the effectiveness of this method.

Multiple Subformulae Cooperative Control for Multiagent Systems Under Conflicting Signal Temporal Logic Tasks

This paper studies the temporal logic problem for multi-agent systems, where each agent is subject to signal temporal logic (STL) tasks with multiple sub-tasks. In the distributed framework, due to the existence of coupling sub-tasks, the satisfaction of the conjunction of all sub-tasks may be conflicting. A two-step distributed model predictive control (DMPC) strategy is proposed to maximize the amount of the satisfied sub-tasks and minimize the violation degree of those failed sub-tasks. In step one, a novel robustness metric of STL is proposed to measure whether each sub-task is satisfied or not, and is directly incorporated into the DMPC optimization problem to determine the satisfiability of each sub-task. Based on the planning results of step one, a DMPC optimization problem with a short planning horizon is designed in step two to minimize the violation degree of the unsatisfiable sub-tasks while ensuring the satisfaction of those satisfiable ones. All agents solve their two-step DMPC problems sequentially to realize the satisfaction verification of the coupled sub-tasks at each time instant. Further, the soundness and feasibility of the two-step DMPC algorithm are formally guaranteed. Simulations and experiments illustrate the effectiveness of the proposed algorithm.

IoT-based simulation of flocking algorithm

The study of animal groups’ flocking behavior is of great significance to the study of multi-agent system. Flocking algorithm is a typical algorithm for group control based on animal group behavior. The research on flocking algorithm is mature, but there is little research on the implementation of Flocking algorithm using digital twin. Digital twins, as a key technology in the direction of IoT, is potentially valuable for improving the efficiency of cloud and devices. Therefore, the main research goal of this paper is to simulate the flocking phenomenon in nature by using digital twins and flocking algorithm to realize the cooperative control of multi-agent systems under the scenarios of obstacle-free and obstacle. In this paper, the author uses digital twins and flocking algorithm to achieve simulations of animal flocking Behaviors. The final simulation results show that the Multi-agent system can converge agents and reach consensus, and finally cross the obstacles to reach the target node, which verifies the rationality and effectiveness of the algorithm. Through the way of digital twins, the burden of the intelligence body’s computation on obstacle identification can be reduced, and the synchronization efficiency of the cloud and the device can be effectively elevated, thus in a practical sense, favoring the deployment implementation.

Distributed Active Fault-Tolerant Cooperative Control for Multiagent Systems With Communication Delays and External Disturbances.

This article investigates the distributed active fault-tolerant cooperative control problem for leader-follower multiagent systems (MASs) in the presence of multiple faults, communication delays, and external disturbances. A new distributed consensus protocol is put forward to ensure the state consensus of MASs, which can be served as a nominal controller in fault-free cases with communication delays and external disturbances. A novel distributed time-delay intermediate observer, which can estimate system states and multiple faults simultaneously, is derived based on the time-delay closed-loop system equation. By integrating a fault compensation mechanism into the nominal controller, a distributed active fault-tolerant consensus controller is constructed for the follower agents to eliminate the adverse effects of multiple faults. Simulation examples are provided to demonstrate the effectiveness of the proposed method.

Cooperative Control of Uncertain Multiagent Systems via Distributed Gaussian Processes

For single agent systems, probabilistic machine learning techniques such as Gaussian process regression have been shown to be suitable methods for inferring models of unknown nonlinearities, which can be employed to improve the performance of control laws. While this approach can be extended to the cooperative control of multi-agent systems, it leads to a decentralized learning of the unknown nonlinearity, i.e., each agent independently infers a model. However, a decentralized learning approach can potentially lead to poor control performance, since the models of individual agents are often accurate in merely a small region of the state space. In order to overcome this issue, we propose a novel method for the distributed aggregation of Gaussian process models, and extend probabilistic error bounds for Gaussian process regression to the proposed approach. Based on this distributed learning method, we develop a cooperative tracking control law for leader-follower consensus of multi-agent systems with partially unknown, higher-order, control-affine dynamics, and analyze its stability using Lyapunov theory. The effectiveness of the proposed methods is demonstrated in numerical evaluations.

Controllability of multi-agent systems with input and communication delays

Distributed cooperative control of multi-agent systems is broadly applied in artificial intelligence in which time delay is of great concern because of its ubiquitous. This paper considers the controllability of leader-follower multi-agent systems with input and communication delays. For the first-order systems with input delay, neighbor-based protocol is adopted to realize the interactions among agents, yielding a system with delay existed in state and control input. New notions of interval controllability and interval structural controllability for the system are defined. Algebraic criterion is established for interval controllability, and graph-theoretic interpretation is put forward for the interval structural controllability. Results imply that input delay of the multi-agent systems has significant influence on the interval controllability and interval structural controllability. Corresponding conclusions are generalized to the first-order systems and the high-order ones with communication delays, respectively. Example is attached to illustrate the work.

Order-Preserved Preset-Time Cooperative Control: A Monotone System-Based Approach

This paper is concerned with order-preserved preset-time cooperative control of multi-agent systems with directed graphs. A novel monotone system-based approach is proposed to preserve the initial order of agents while guaranteeing the preset-time state agreement. Specifically, three different distributed controllers together with sufficient conditions are designed to realize leaderless consensus, leader-following consensus, and containment control, respectively. The proposed controllers facilitate preset-time deployment of agents in practical scenarios with collision avoidance requirement. Comparison studies through a numerical example are carried out to illustrate the effectiveness of the proposed controllers.

A new perspective on cooperative control of multi-agent systems through different types of graph Laplacians

In the field of systems and control, many cooperative control problems of multi-agent systems have been actively studied in the past two decades. This article aims to organize extensive existing work on different cooperative control problems into three categories, based on three different types of graph Laplacian matrices involved. A Laplacian matrix is an important representation of graph topology, and depending on the field of its entries, there are three types: ordinary Laplacian (non-negative diagonal entries and non-positive off-diagonal entries), signed Laplacian (arbitrary real entries), and complex Laplacian (arbitrary complex entries). Each type of graph Laplacian is useful in modeling and solving a different set of cooperative control problems. In particular, their algebraic properties are fundamental in characterizing stability and performance of the respective solution algorithms. To our best knowledge, organizing the literature on multi-agent cooperative control through the lens of different graph Laplacians is new.

An Event-Triggered Observer and Its Applications in Cooperative Control of Multiagent Systems

This article proposes an event-triggered control design for cooperative control of heterogeneous multiagent systems. The design is serial in nature and consists of the design of a distributed event-triggered observer for each follower agent that estimates the desired information of the leader system in the presence of parametric uncertainty and, based on this observer, the design of a distributed event-triggered controller. A salient feature of our observer design is that it obeys the separation principle in the sense that the event-triggered control can be designed after the observer has been designed. As a result, the observer is applicable to different follower agent dynamics and the event-triggered time sequence for the controller is asynchronous of that of the observer. We implement this design scheme on linear heterogeneous multiagent systems and uncertain Euler–Lagrange multiagent systems, and achieve results that significantly extend the existing ones.

Cooperative control of multi-agent systems: Where additional communication links improve the transient behaviour

The paper presents an interesting structure of cooperative control in which every additional communication link improves the transient behaviour of the overall system. The performance output is the weighted sum of the agent outputs and the transient behaviour is evaluated by a quadratic performance index. The agents may send their control error to a neighbouring agent, which uses this information to modify its own control error, which is the input to the local controller. The main result shows that in a path communication graph with the disturbed agent as the root vertex any additional communication link that prolongates the path improves the overall system performance. This result is demonstrated by its application to a simplified model of an electrical power system.

An Overview of Recent Advances in Distributed Coordination of Multi-Agent Systems

Distributed coordination of multi-agent systems (MASs) has received considerable attention and advanced rapidly in the past years. This paper reviews the recent results on cooperative control of MASs, and in particular cooperative control under various agent dynamics, under communication transmission delays, and under event-triggered strategies. In addition, different coordination tasks, such as consensus, cooperative output regulation, formation control, and containment control, are also taken into consideration. Finally, some open problems on cooperative control of MASs are suggested.

Optimal distributed cooperative control for multi-agent systems with constrains on convergence speed and control input

This paper focuses on the distributed optimal cooperative control for multi-agent systems (MASs) over fixed communication graphs. Firstly, it is proved that the optimal distributed control for the local quadratic performance indexes of all agents is equivalent to solve a globally optimal problem, the resulting optimal Laplacian matrix associates with a complete graph hence the optimal distributed protocols do not exist. Consequently, we turn to solve the globally optimal distributed control problem for the candidate protocols with specified distributed structure. Furthermore, suboptimal fully distributed protocols can be designed when the Laplacian matrix and the initial states of all agents are unavailable. As a simple application, the minimum energy distributed cooperative control problem is addressed. Then, from a practical viewpoint, we solve the distributed optimal control problem with constrains on specified convergence speed and control input. Sufficient conditions for existence of the optimal solutions are derived. Finally, simulation examples are given to verify the effectiveness of the proposed results.

Distributed Estimation of Algebraic Connectivity.

The measurement algebraic connectivity plays an important role in many graph theory-based investigations, such as cooperative control of multiagent systems. In general, the measurement is considered to be centralized. In this article, a distributed model is proposed to estimate the algebraic connectivity (i.e., the second smallest eigenvalue of the corresponding Laplacian matrix) by the approach of distributed estimation via high-pass consensus filters. The global asymptotic convergence of the proposed model is theoretically guaranteed. Numerical examples are shown to verify the theoretical results and the superiority of the proposed distributed model.

Semiglobal Tracking Cooperative Control for Multiagent Systems With Input Saturation: A Multiple Saturation Levels Framework

This article proposes a multiple saturation levels framework to investigate the semiglobal tracking cooperative control problem of multiagent systems with switching communication topologies. This problem is solving by proposing a switching parametric Lyapunov approach and constructing a three-layer nodes model with mirroring nodes to reduce the complexity of controller design in which control inputs for different layers of nodes are with different saturation levels. A metamorphic low-gain feedback consensus protocol is developed, based only on the interaction information of different nodes, to ensure that the states of the followers approach that of the leader, if the dwell time can be reasonably selected and the switching derived topologies have a directed spanning tree. Moreover, the protocol is extended to the derived topology that frequently contains a directed spanning tree. Simulations are presented to verify the effectiveness of the new design techniques.

Event-Triggered Consensus for Euler-Lagrange Systems with Communication Delay

Distributed cooperative control of multi-agent systems, typically requires some form of information exchange in order to achieve coordination between individual agents. Especially in wireless communication systems, communication delays can lead to instability and can not be neglected during the design of the control law. We propose a novel coordination scheme for Euler-Lagrange systems taking into account constant communication delays. Additionally, in order to save limited resources, we propose an event-triggered strategy for the communication between agents, as well as for the local actuator updates of the individual agents. We show that with the proposed algorithms a stable interaction in consensus tasks can be guaranteed. Simulations illustrate the theoretical results.

Interaction Matrix Based Analysis and Asymptotic Cooperative Control of Multi-agent Systems

In this paper, we investigate a decentralized asymptotic cooperative control problem of multi-agent systems with leader-follower configuration. We firstly develop a new method using a proposed “interaction matrix” for the analysis of cooperation convergence of multi-agent systems, i.e. both consensus of the agents states and trajectory tracking of the whole group can be instantaneously concluded only by observing the minimum eigenvalue of the interaction matrix. For a multi-agent system, the external given desired trajectory can be partially obtained (through sensing or detecting) by the leaders, but higher-order derivatives such as acceleration and jerk of the desired trajectory cannot be obtained. In this case, by using some conventional control methods, the trajectory tracking performance is always not satisfactory when a trajectory varies aggressively w.r.t. time. For the sake of asymptotic tracking of an arbitrary given external trajectory of a multi-agent system, we develop a nonlinear cooperative controller based on the robust integral of signum of cooperative error (RISCE) technique, where the interaction matrix is used. The simulation results show asymptotic convergence of cooperation by using the proposed control, and better performance compared to composited nonlinear feedback based PD (CNF-PD) control.