Consensus Control Research Articles

Event-triggered-based encoding–decoding consensus control of continuous-time multi-agent systems under DoS attacks

The security of multi-agent systems (MASs) is important particularly when considering the potential threat posed by malicious attackers. Specifically, network attacks and information theft caused by attackers have garnered significant attention due to their potential to destroy the consensus of the system. Therefore, this paper presents a novel approach to achieve leader-following consensus control of continuous-time MASs under DoS attacks, leveraging event-triggered encoding and decoding techniques. When the DoS attacks occur, the transmitted information between agents becomes unavailable. To address this challenge, a buffer is introduced to store the latest transmission data and then the stored data can be used when the DoS attacks exist. We also propose an event-triggered encoding–decoding strategy that can be employed for privacy protection in linear continuous-time MASs. The transmitted data is encoded as a codeword before transmitting, and the encoder updates it when event-triggered conditions are met. The receiving end decodes the data upon receipt. Furthermore, a distributed switching controller is designed using the encoded and stored state values to achieve leader-following consensus. The control gain is calculated to obtain sufficient conditions for DoS attacks through the linear matrix inequalities (LMI) and Lyapunov functions. The boundedness of transmitted data and the Zeno phenomenon are also analyzed. Finally, numerical simulations are conducted to demonstrate the effectiveness of the proposed control algorithm.

Nonsingular adaptive finite-time consensus control for uncertain nonlinear multi-agent systems with input quantization

This paperinvestigates the distributed consensus control for nonlinear multi-agent systems with input quantization in a directed communication topology. A nonsingular adaptive finite-time control (NAFTC) scheme is proposed by combining the filtered backstepping method with neural network control. Both chattering and singularity problems of the control signals are avoided thanks to its [Formula: see text] continuity, so that the contradiction between discontinuous- and [Formula: see text] continuous finite-time control with input quantization does not exist in our scheme. At the same time, a novel inequality for the input-output relationship of hysteresis quantizer is derived, which can simply handle the input quantization without requiring additional stability analysis. What is more, some common assumptions, such as Lipschitz assumption and Holder-continuity assumption, are not required in our scheme. Finally, stability analysis and numerical simulation are provided.

Energy management and SoC balancing of distributed batteries in AC microgrids using consensus tracking control

This paper proposes a consensus tracking control method for energy management and state-of-charge (SoC) balancing of energy storage batteries in the grid-connected mode of AC microgrids. By adding a target power term to the consensus average control law, taking into account battery energy storage system (BESS) with various SoCs and various capacities, the stability and robustness of the proposed consensus control approach are analytically validated. Simulations of a state–space battery model and a detailed dynamic model of batteries in grid-connected mode are constructed in a Matlab environment. A detailed study of weighting matrices of the control approach is presented, and their suitable range is identified. The results show that the proposed method could effectively balance SoC for batteries of equal and different capacities, allowing the battery with the higher capacity to charge and discharge more than the others at the same time while still maintaining the target power. Furthermore, the controller successfully prevented deep discharge or overcharging of the batteries by disconnecting them when they reached their SoC limitations and compensating for this disconnection by raising their target power to maintain the same load.

Model‐free distributed optimal control for general discrete‐time linear systems using reinforcement learning

AbstractThis article proposes a novel data‐driven framework of distributed optimal consensus for discrete‐time linear multi‐agent systems under general digraphs. A fully distributed control protocol is proposed by using linear quadratic regulator approach, which is proved to be a sufficient and necessary condition for optimal control of multi‐agent systems through dynamic programming and minimum principle. Moreover, the control protocol can be constructed by using local information with the aid of the solution of the algebraic Riccati equation (ARE). Based on the Q‐learning method, a reinforcement learning framework is presented to find the solution of the ARE in a data‐driven way, in which we only need to collect information from an arbitrary follower to learn the feedback gain matrix. Thus, the multi‐agent system can achieve distributed optimal consensus when system dynamics and global information are completely unavailable. For output feedback cases, accurate state information estimation is established such that optimal consensus control is realized. Moreover, the data‐driven optimal consensus method designed in this article is applicable to general digraph that contains a directed spanning tree. Finally, numerical simulations verify the validity of the proposed optimal control protocols and data‐driven framework.

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.

Proactive cooperative consensus control for a class of human-in-the-loop multi-agent systems with human time-delays

In this study, we consider a class of human-in-the-loop (HiTL) multi-agent systems that divide agents into two parts: the nonautonomous agents controlled by human operators, and the autonomous agents. First, the human operators’ models are incorporated into the multi-agent system for constructing an HiTL multi-agent system model. Next, the cooperative consensus control problem is explored for the HiTL multi-agent system, and a method is proposed that enables autonomous agents to proactively collaborate with human-controlled agents in order to mitigate the effects of human time-delays. We first obtain the consensus sufficient condition for the HiTL multi-agent system without human time-delays, and then give the method by only designing the autonomous agents’ control gains to guarantee consensus. Subsequently, the delay-dependent consensus sufficient condition for the HiTL multi-agent system with human time-delays is also offered, and the method of controller design is updated for this case. Finally, a simulation experiment was designed, and the simulation results are presented to illustrate the effectiveness of the proposed methods.

Optimized backstepping consensus control using adaptive observer-critic–actor reinforcement learning for strict-feedback multi-agent systems

In this paper, we present a novel control method, combining observer-based optimized backstepping (OB) control, reinforcement learning (RL) strategy, and adaptive neural networks (NN), for strict-feedback multi-agent systems with unmeasurable states. The primary objective is to enhance the overall system backstepping controller by optimizing both virtual and actual controllers for corresponding subsystems. To achieve this, we develop an observer-critic–actor RL approach based on NN approximation in all backstepping steps. The observers are utilized to estimate the unmeasurable system states, while the critic–actor algorithm assesses the control performance and executes control actions. Our optimized control method offers advantages such as not requiring the state observer to satisfy the Hurwitz equation. Additionally, our designed RL algorithm is simple due to the critic–actor adaptive laws obtained by means of the negative gradient of a simple positive function associated with the partial derivative of Hamilton–Jacobi–Bellman (HJB) equation. Consequently, our proposed control method ensures that all error states of the multi-agent systems are semi-globally uniformly ultimately bounded (SGUUB), and that all outputs can synchronously follow the reference signal with desired accuracy. Finally, we demonstrate the efficacy of our control strategy by means of theoretical analysis and simulation results.

Adaptive cluster consensus control of nonlinear multi‐agent system via the dynamic event‐triggered strategy

SummaryIn this article, the cluster consensus problems of two classes of nonlinear multi‐agent systems (NMASs) are studied under the dynamic event‐triggered control (ETC) strategy, where cooperative‐competitive interactions among agents and high‐order dynamics are considered. First, two dynamic ETC protocols are developed to realize the cluster synchronization of NMASs. Different from the static ETC, a dynamic parameter regulated by an adaptive law is used in place of a constant which can improve the trigger time intervals and reduce the energy consumption. Subsequently, an adaptive control mechanism based on the proposed ETC strategy is designed to adjust the controller parameter for agents. Some sufficient conditions for achieving the cluster consensus of NMASs are derived by using the Lyapunov stability theory and algebraic graph theory. Moreover, it is proved in theory that Zeno phenomenon can be excluded with these control schemes. Finally, two simulation examples validate the effectiveness of the theoretical results.

Dynamic event‐triggered fixed‐time average consensus for general linear multi‐agent systems under switching topologies

AbstractThis article studies the practical fixed‐time average consensus problem of general linear multi‐agent systems under switching topologies. First, a distributed fixed‐time consensus control protocol based on dynamic event‐triggered mechanism is designed by using the local information exchange between neighboring agents. The protocol introduces an internal dynamic variable and the triggering condition changes in real time based on the dynamic variable obtained online, which can significantly reduce the number of triggering events and effectively decrease the energy consumption of the multi‐agent system. Then, the sufficient conditions for the multi‐agent system to solve the practical fixed‐time average consensus problem are given, and it is proved that there is no Zeno behavior in the system. Furthermore, a self‐triggered control scheme is proposed such that the neighboring agents communicate with each other only when one of them is triggered. In other words, this scheme does not require continuous communication between neighboring agents and thus reduces communication cost of the system. Finally, the simulation results verify the effectiveness of the proposed method.

Distributed Model Predictive Control for Consensus of Multi-Agent Systems With Connectivity Maintenance

Distributed Model Predictive Control for Consensus of Multi-Agent Systems With Connectivity Maintenance

Resilient Consensus Control for Heterogeneous Multiagent Systems via Multiround Attack Detection and Isolation Algorithm

Resilient Consensus Control for Heterogeneous Multiagent Systems via Multiround Attack Detection and Isolation Algorithm

Global Distributed Fault-Tolerant Consensus Control of Nonlinear Delayed Multiagent Systems With Hybrid Faults

Global Distributed Fault-Tolerant Consensus Control of Nonlinear Delayed Multiagent Systems With Hybrid Faults

On Generalized Homogeneous Leader-Following Consensus Control for Multiagent Systems

On Generalized Homogeneous Leader-Following Consensus Control for Multiagent Systems

Distributed Constrained Consensus of Multiagent Systems With Uncertainties and Disturbances Under Switching Directed Graphs

This paper provides a distributed leaderless consensus control framework for nonlinear multi-agent systems with time-varying asymmetric state constraints, uncertainties, and disturbances under switching directed graphs. In such a framework, original constrained states of agents are first transformed into free states in a transformed state space. To deal with switching directed graphs, we drive agents towards consensus in the transformed space by leveraging a model reference control scheme, and it is sufficient that the original states reach consensus strictly subject to the time-varying constraints under mild assumptions. A single-layer neural network with weights adapted online is leveraged to approximate the uncertainties in agent dynamics. For external disturbances and reconstruction errors in the approximation, we introduce a robust term with an adaptive gain for compensation. Distributed consensus algorithms are proposed, respectively, for multi-agent systems with first- or second-order dynamics. We prove convergence to consensus via Lyapunov analysis and study the proposed algorithms' performance using numerical simulations.

Event-Triggered Guaranteed Cost Secure Consensus Control of Multiagent Systems Under DoS Attacks Over Multiple Communication Channels

Event-Triggered Guaranteed Cost Secure Consensus Control of Multiagent Systems Under DoS Attacks Over Multiple Communication Channels

Composite adaptive fuzzy bipartite consensus of fractional-order multiagent systems with a switched event-triggered mechanism

This paper focuses on online recorded-data-based composite adaptive fuzzy bipartite consensus control for uncertain fractional-order multiagent systems with interconnected terms and external disturbances by employing a switched-threshold-based event-triggered mechanism (ETM) under the backstepping structure. Fuzzy logic system is used as a universal function approximation to deal with function uncertainties that are not prone to model in the system. A new composite learning adaptive parameter design scheme that synthesizes both prediction error and tracking error is developed to enhance the tracking performance, where the prediction error is raised from the utilization of online recorded data and instantaneous data. A unique switched-threshold-based ETM is introduced, in which the information transmission between the sensor and the controller is imposed on one of the individuals. One merit of this work consists in that it can automatically and rapidly switch and adjust between the fixed threshold and relative threshold ETM according to the amplitude of input signals to balance the network resources and impede the occurrence of pulse phenomenon. In addition, it is theoretically proven that the proposed scheme can ensure that all internal signals of the closed-loop system are bounded and achieve local bipartite consistent errors through the fractional Lyapunov stability criterion. Finally, a numerical example is provided to confirm the feasibility of the proposed approach.

Refined Dynamic Event-Triggering Cluster Consensus of Multiagent Systems With Fixed/Switching Topology.

This article is concerned with cluster consensus control of multiagent systems (MASs) with the fixed/switching topology under a dynamic event-trigger (DET) mechanism. A refined sampled-data-based DET scheme is proposed by introducing two dynamically adjusting threshold parameters to distinguish the different transmission requirements for neighboring agents intra and outer cluster. Faced with the difficulties of acquiring full state information among spatially distributed agents, output feedback is employed to construct cooperative control protocols. Both fixed and switching topologies are considered to execute the designed DET-based cooperative cluster consensus control protocols. By constructing appropriate Lyapunov-Krasovskii functionals (LKFs), some sufficient criteria in terms of matrix inequalities for the cluster consensus of MASs are derived, which can ensure that the error system with the proposed DET-based control strategy is asymptotically stable. Facing the nonconvex issue induced by output feedback, a particle swarm optimization (PSO)-based control design algorithm is novelly developed to calculate the control gains and event-triggering parameters jointly based on the derived stability criteria. The elements of the matrix variables are valued stochastically in certain ranges and the fitness function is designed as the accumulation of the weighting value of each matrix inequality. Finally, an application of multiple satellites formation flying is applied to numerically illustrate the effectiveness of the cluster consensus control strategy with the designed DET mechanism.

Distributed Event-Triggered Simultaneous Fault Detection and Consensus Control for Multiple Lur'e Systems

Distributed Event-Triggered Simultaneous Fault Detection and Consensus Control for Multiple Lur'e Systems

Dynamic Event-Triggered Consensus Control for Input Constrained Multi-Agent Systems with a Designable Minimum Inter-Event Time

Dynamic Event-Triggered Consensus Control for Input Constrained Multi-Agent Systems with a Designable Minimum Inter-Event Time

Fixed-Time Fault-Tolerant Consensus Control for High-Order Nonlinear Multiagent Systems Under Directed Topology

This paper addresses the fixed-time fault-tolerant consensus control problem for high-order nonstrict-feedback nonlinear multi-agent systems under directed topology. A distributed high-order fixed-time observer is proposed to obtain the accurate estimations of the full states of the leader. Different from the fixed-time observer design methods, a much more concise estimate for the upper bound of the convergence time is provided. Based on the fixed-time observer and adding a power technique, a fixed-time tracking protocol is designed for a group of followers subject to unknown nonidentical dynamics and unpredictable actuation faults. It is shown that there is no need to know the information of the nonstrict-feedback nonlinear dynamics, the control gains, and the actuation faults in the control design and implementation completely, which is deemed favorable in practice. Simulation results are provided to validate the effectiveness of the developed control scheme.