Heterogeneous Multi-agent Systems Research Articles

Optimal bipartite consensus control for heterogeneous unknown multi-agent systems via reinforcement learning

This study focuses on addressing optimal bipartite consensus control (OBCC) problems in heterogeneous multi-agent systems (MASs) without relying on the agents' dynamics. Motivated by the need for model-free and optimal consensus control in complex MASs, a novel distributed scheme utilizing reinforcement learning (RL) is proposed to overcome these challenges. The MAS network is randomly partitioned into sub-networks where agents collaborate within each subgroup to attain tracking control and ensure convergence of positions and speeds to a common value. However, agents from distinct subgroups compete to achieve diverse tracking objectives. Furthermore, the heterogeneous MASs considered have unknown first and second-order dynamics, adding to the complexity of the problem. To address the OBCC issue, the policy iteration (PI) algorithm is used to acquire solutions for discrete-time Hamilton-Jacobi-Bellman (HJB) equations while implementing a data-driven actor-critic neural network (ACNN) framework. Ultimately, the accuracy of our proposed approach is confirmed through the presentation of numerical simulations.

Quantised adaptive consensus control of heterogeneous nonlinear multi-agent systems under external disturbance

This paper is primarily devoted to the distributed consensus tracking control for heterogeneous nonlinear multi-agent systems (MASs) with a directed communication topology. Taking the considered heterogeneous nonlinear MASs own unknown nonlinear term and quantised input signals, the adaptive radial basis function neural networks (RBFNNs)-based approximator is utilised to tackle these interesting challenges. Next, considering the boundness of disturbances, the upper bound of time-varying disturbance is utilised in the controller design. Then, a distributed dynamic compensator is developed to complete coordination task, which only requires to exchange output information via the communication network. In addition, the compensator transforms the adaptive output consensus problem into asymptotic tracking problem, without needing extra stability conditions. Finally, by means of the Lyapunov stability theory, the satisfying result of the consensus error converging to zero asymptotically can be obtained. Through simulations, the effectiveness of the developed protocol is illustrated.

Data-driven adaptive consensus control for heterogeneous nonlinear Multi-Agent Systems using online reinforcement learning

In this paper, a distributed control algorithm based on a data-driven approach is developed to solve the consensus control of heterogeneous nonlinear Multi-Agent Systems (MAS). The consensus obtained from the solution of the Hamilton–Jacobi–Bellman (HJB) equation is challenging for unknown nonlinear systems. To address this issue, improved online reinforcement learning (RL) is employed to generate an approximate solution for each agent to achieve consensus. Unlike model-based RL and traditional algorithms, this method leverages I/O data to guide the learning of policies without any prior knowledge of agent dynamics. Furthermore, the adaptability of algorithm to heterogeneous nonlinear agents is enhanced by implementing online updates to the control strategy and dynamic linearization (DL). The convergence analysis of the algorithm is provided, along with the impact of the learning rateparameters on the consensus of MAS. By comparing with other data-driven methods, simulations are conducted to verify the stability and adaptability of the proposed algorithm.

Fixed-time consensus control for uncertain heterogeneous multi-agent systems with high-order dynamics and time-varying delay under generic topologies

This article is concerned with the fixed-time control problem of uncertain high-order heterogeneous multi-agent systems with time-varying delays for both directed and undirected topologies. Applying the free-weighting matrix approach, a sliding mode controller is designed through the Lyapunov–Krasovskii functional and the conditions are derived with the help of linear matrix inequalities to attain fixed-time consensus tacking control of such systems. Two numerical simulations are included to verify the capability of the derived control law with respect to different initializations.

Event-based Consensus of Double-integral Heterogeneous Hybrid Multi-agent Systems With Communication Delays

Event-based Consensus of Double-integral Heterogeneous Hybrid Multi-agent Systems With Communication Delays

Semi-Global Bipartite Fault-Tolerant Containment Control for Heterogeneous Multiagent Systems With Antagonistic Communication Networks and Input Saturation.

Semi-global bipartite fault-tolerant containment control framework on antagonistic communication networks is proposed in this article for heterogeneous multiagent systems (MASs) under the influence of input saturation and actuator faults. An observer is constructed to estimate the leaders' states on signed digraph, where the communication networks are antagonistic. A fully distributed virtual control approach is developed to acquire the containment trajectory. Based on the observer, a semi-global containment control method is developed to compensate for the detrimental impacts of both input saturation and actuator faults. Besides, the dynamics and state-space dimensions of the agents can be different. The proposed framework overcomes two drawbacks of the conventional containment control: 1) the containment trajectory is obtained under general antagonistic communication networks, which is more general in engineering applications and 2) both actuator faults and input saturation are solved for heterogeneous agents, which relaxes the limitation of homogeneous dynamics. Finally, a simulation example is conducted to test and verify the feasibility of the proposed method framework.

Resilient Output Containment Control of Heterogeneous Multiagent Systems Against Composite Attacks: A Digital Twin Approach.

This article delves into the distributed resilient output containment control of heterogeneous multiagent systems against composite attacks, including Denial-of-Service (DoS) attacks, false-data injection (FDI) attacks, camouflage attacks, and actuation attacks. Inspired by digital twin technology, a twin layer (TL) with higher security and privacy is employed to decouple the above problem into two tasks: 1) defense protocols against DoS attacks on TL and 2) defense protocols against actuation attacks on the cyber-physical layer (CPL). Initially, considering modeling errors of leader dynamics, distributed observers are introduced to reconstruct the leader dynamics for each follower on TL under DoS attacks. Subsequently, distributed estimators are utilized to estimate follower states based on the reconstructed leader dynamics on the TL. Then, decentralized solvers are designed to calculate the output regulator equations on CPL by using the reconstructed leader dynamics. Simultaneously, decentralized adaptive attack-resilient control schemes are proposed to resist unbounded actuation attacks on the CPL. Furthermore, the aforementioned control protocols are applied to demonstrate that the followers can achieve uniformly ultimately bounded (UUB) convergence, with the upper bound of the UUB convergence being explicitly determined. Finally, we present a simulation example and an experiment to show the effectiveness of the proposed control scheme.

Neural-Network-Based Adaptive Fault-Tolerant Cooperative Control of Heterogeneous Multiagent Systems With Multiple Faults and DoS Attacks.

In this article, the issue of adaptive fault-tolerant cooperative control is addressed for heterogeneous multiple unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) with actuator faults and sensor faults under denial-of-service (DoS) attacks. First, a unified control model with actuator faults and sensor faults is developed based on the dynamic models of the UAVs and UGVs. To handle the difficulty introduced by the nonlinear term, a neural-network-based switching-type observer is established to obtain the unmeasured state variables when DoS attacks are active. Then, the fault-tolerant cooperative control scheme is presented by utilizing an adaptive backstepping control algorithm under DoS attacks. According to Lyapunov stability theory and improved average dwell time method by integrating the duration and frequency characteristics of DoS attacks, the stability of the closed-loop system is proved. In addition, all vehicles can track their individual references, while the synchronized tracking errors among vehicles are uniformly ultimately bounded. Finally, simulation studies are given to demonstrate the effectiveness of the proposed method.

Event-Based Distributed Average Tracking for Second-Order Heterogeneous Multiagent Systems With Uncertain Dynamics

Event-Based Distributed Average Tracking for Second-Order Heterogeneous Multiagent Systems With Uncertain Dynamics

Fully Data-Driven Robust Output Formation Tracking Control for Heterogeneous Multiagent System With Multiple Leaders and Actuator Faults.

This article investigates a fully data-driven method to solve the robust output formation tracking control problem for the multiagent system (MAS) under actuator faults. The outputs of the followers are controlled to track those of multiple leaders with respect to a convex point while achieving an expected time-varying formation. To obviate the requirement of various system prior knowledge in typical MAS control, a hierarchical frame is developed with three learning and control stages using the online measured data. First, a distributed adaptive observer is designed to coordinate the state convex of multiple leaders while estimating unknown dynamics. The adaptive mechanism relaxes the demand for global topology. Second, by collecting and reusing the online system data, an off-policy reinforcement learning (RL) method is proposed in a continuous form to acquire nominal feedback gains from partial observations of the followers. Essential system models are learned along with the RL process, while solutions to the output regulation equations are implicitly obtained. Third, a comprehensive robust controller is further presented based on the previous learning results. To address the actuator faults with efficiency loss and bias, the adaptive neural networks and robust compensations are utilized in a model-free manner. The output formation tracking is achieved under a derived feasibility condition while stabilities of the learning and control methods are analyzed. Finally, simulation results demonstrate the validity of this fully data-driven control frame.

Finite-time Consensus of Heterogeneous Multi-agent Systems by Integral Sliding Mode Control

Finite-time Consensus of Heterogeneous Multi-agent Systems by Integral Sliding Mode Control

Cooperative control for heterogeneous multi-agent systems: progress, applications, and challenges

Cooperative control for heterogeneous multi-agent systems: progress, applications, and challenges

Privacy-preserving bipartite output consensus for continuous-time heterogeneous multi-agent systems via event-triggered impulsive control

Privacy-preserving bipartite output consensus for continuous-time heterogeneous multi-agent systems via event-triggered impulsive control

Distributed optimal output synchronization of heterogeneous multiagent systems with communication delays

Distributed optimal output synchronization of heterogeneous multiagent systems with communication delays

Fully distributed event-triggered formation-containment control for heterogeneous multi-agent systems with guaranteed positive MTI

In this work, we address the event-triggered output formation-containment control problem for heterogeneous multi-agent systems with multiple leaders. The aim is to achieve formation of follower agents and containment of multiple leaders by designing fully distributed event-triggered control strategy, while the minimum triggering interval (MTI) is guaranteed to be strictly positive, which is a stronger condition than simply excluding Zeno behavior. First, an output feedback fully distributed control law is proposed. Then, a novel event-triggering mechanism (ETM) is designed by introducing an auxiliary function with time-varying coefficients. By means of the auxiliary function, the time it takes for the auxiliary function to decrease from given initial condition to zero can be imposed to be the MTI. Moreover, a novel Lyapunov function depending on the auxiliary function and error term is constructed for stability analysis and control design. Finally, the effectiveness of the obtained results is illustrated by simulation results.

Adaptive bipartite output containment control of heterogeneous multi‐agent systems against FDI attacks

AbstractThis paper researches the adaptive bipartite output containment control of heterogeneous multi‐agent systems (HMASs) against false data injection (FDI) attacks. The malicious adversaries could inject attacks on the actuator to maximize damage to the HMASs, which in turn causes the compromised followers to be unable to track the output states of the leaders. To address this issue, a resilient layer is introduced that prevents attack signals from affecting the states. Moreover, an adaptive compensator is ingeniously devised to counteract these attacks, thereby ensuring that both cooperative and antagonistic agents adhere to distinct convex hulls. Leveraging the output regulation approach, conducting Lyapunov stability analysis, and drawing upon other pertinent methodologies, we have systematically constructed a robust framework for the realization of bipartite output containment control. Ultimately, the validity of the analytical findings is confirmed through a series of rigorous numerical simulations.

Finite-Time Time-Varying Formation Tracking for Heterogeneous Nonlinear Multiagent Systems Using Adaptive Output Regulation.

The finite-time output time-varying formation tracking (TVFT) problem for heterogeneous nonlinear multiagent system (MAS) is investigated in this article, where the dynamics of the agents can be nonidentical, and leader's input is unknown. The target of this article is that the outputs of followers need to track leader's output and realize the desired formation in finite time. First, for removing the assumption that all agents are required to know the information of leader's system matrices and the upper boundary of its unknown control input in previous studies, a kind of finite-time observer is constructed by exploiting the neighboring information, which can estimate not only the leader's state and system matrices but also can compensate for the effects of unknown input. On the basis of the developed finite-time observers and adaptive output regulation method, a novel finite-time distributed output TVFT controller is proposed with the help of the technique of coordinate transformation by introducing an extra variable, which removes the assumption that the generalized inverse matrix of follows' input matrix needs to be found in the existing results. By means of the Lyapunov and finite-time stability theory, it is proven that the expected finite-time output TVFT can be realized by the considered heterogeneous nonlinear MASs within a finite time. Finally, simulation results demonstrate the efficacy of the proposed approach.

Model-Free Containment Control of Fully Heterogeneous Linear Multiagent Systems

Model-Free Containment Control of Fully Heterogeneous Linear Multiagent Systems

Optimal Formation Control of Second-Order Heterogeneous Multiagent Systems Using Adaptive Predefined-Time Strategy

Optimal Formation Control of Second-Order Heterogeneous Multiagent Systems Using Adaptive Predefined-Time Strategy

Resilient Output Formation-Tracking of Heterogeneous Multiagent Systems Against General Byzantine Attacks: A Twin-Layer Approach.

This work solves the countermeasure design problems of distributed resilient output time-varying formation-tracking (TVFT) of heterogeneous multiagent systems (MASs) against general Byzantine attacks (GBAs). Inspired by the concept of Digital Twin, a hierarchical protocol equipped with a twin layer (TL) is proposed, which decouples the above problem into the defense against Byzantine edge attacks (BEAs) on the TL and the defense against Byzantine node attacks (BNAs) on the cyber-physical layer (CPL). First, a secure TL with respect to (w.r.t.) the high-order leader dynamics is designed, which achieves resilient estimation against BEAs. A trusted-node strategy against BEAs is proposed, which promotes network resilience by protecting almost the smallest fraction of crucial nodes on the TL. It is proven that strongly (2f+1) -robustness w.r.t. the above trusted nodes is sufficient for the resilient estimation performance of the TL. Second, a decentralized adaptive and chattering-free controller against potentially unbounded BNAs is designed on the CPL. This controller has the merit of uniformly ultimately bounded (UUB) convergence and an assignable exponential decay rate when converging into the above UUB bound. To the best of our knowledge, this article is the first to achieve resilient output TVFT against GBAs, rather than under GBAs. Finally, the practicability and validity of this new hierarchical protocol are illustrated via a simulation example.