Control Of Heterogeneous Multi-agent Systems Research Articles

Adaptive Event-Triggered Time-Varying Output Group Formation Containment Control of Heterogeneous Multiagent Systems

Adaptive Event-Triggered Time-Varying Output Group Formation Containment Control of Heterogeneous Multiagent Systems

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.

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

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

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.

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

Funnel-Based Predefined-Time Containment Control of Heterogeneous Multiagent Systems With Sensor and Actuator Faults

Funnel-Based Predefined-Time Containment Control of Heterogeneous Multiagent Systems With Sensor and Actuator Faults

Data-Efficient Off-Policy Learning for Distributed Optimal Tracking Control of HMAS With Unidentified Exosystem Dynamics.

In this article, a data-efficient off-policy reinforcement learning (RL) approach is proposed for distributed output tracking control of heterogeneous multiagent systems (HMASs) using approximate dynamic programming (ADP). Different from existing results that the kinematic model of the exosystem is addressable to partial or all agents, the dynamics of the exosystem are assumed to be completely unknown for all agents in this article. To solve this difficulty, an identifiable algorithm using the experience-replay method is designed for each agent to identify the system matrices of the novel reference model instead of the original exosystem. Then, an output-based distributed adaptive output observer is proposed to provide the estimations of the leader, and the proposed observer not only has a low dimension and less data transmission among agents but also is implemented in a fully distributed way. Besides, a data-efficient RL algorithm is given to design the optimal controller offline along with the system trajectories without solving output regulator equations. An ADP approach is developed to iteratively solve game algebraic Riccati equations (GAREs) using online information of state and input in an online way, which relaxes the requirement of knowing prior knowledge of agents' system matrices in an offline way. Finally, a numerical example is provided to verify the effectiveness of theoretical analysis.

Impulsive Layered Control of Heterogeneous Multi-Agent Systems Under Limited Communication

Impulsive Layered Control of Heterogeneous Multi-Agent Systems Under Limited Communication

Data-Based Collaborative Learning for Multiagent Systems Under Distributed Denial-of-Service Attacks

This paper employs a reinforcement learning (RL) technique to investigate the distributed output tracking control of heterogeneous multiagent systems (MASs) under multiple denial-of-service (DoS) attacks. Different from existing results where the dynamic of the leader is known for partial or all agents, the leader’s system matrix is completely unknown for each follower in this paper. To learn the leader system matrix, a data-based learning mechanism is first proposed using the idea of the data-driven method. Then, under the data-based learning mechanism, a resilient predictor subject to multiple DoS attacks is exploited to provide the estimation of the leader’s state for each agent, where adversaries attack different communication links independently. Moreover, a resilient dynamic output feedback controller is proposed to solve the output tracking control problem based on the output regulation theory. To consider the transient responses of agents, a RL-based dynamic output feedback controller is developed to realize the optimal output tracking control by solving discounted algebraic Riccati equations (AREs) in both offline and online ways. Theoretical analysis shows that the secure output tracking control of MASs can be achieved under the proposed data-based resilient learning control algorithm. Finally, a numerical example is provided to verify the effectiveness of theoretical analysis.

Prediction-based scaled consensus control for heterogeneous multi-agent systems under switching topologies

Prediction-based scaled consensus control for heterogeneous multi-agent systems under switching topologies

Cooperative control of heterogeneous multi-agent systems under spatiotemporal constraints

A current trend in research on multi-agent control systems is to consider high-level task specifications that go beyond traditional control objectives and take into account the heterogeneity of each agent in the system, i.e., the different capabilities of the agents in terms of actuation, sensing, communication and computation. This article provides an overview of our work on the problem of control of heterogeneous multi-agent systems under both spatial and temporal constraints as well as our perspective on the challenges and open problems associated with the consideration of such spatiotemporal constraints. Initially, we review a set of control strategies introduced by the authors addressing the satisfaction of cooperative tasks such as formation control as well as individual objectives such as reference tracking. The satisfaction of those objectives is ensured using prescribed performance control. Building upon these approaches we then review recent results on control under high-level spatiotemporal objectives expressed in Signal Temporal Logic, a formal language that allows to express complex spatial tasks that must be satisfied within pre-defined deadlines. Theoretical results considering multi-agent systems with various capabilities under spatiotemporal constraints are presented.

Resilient Containment Control of Heterogeneous Multi-Agent Systems Against Unbounded Attacks on Sensors and Actuators

Resilient Containment Control of Heterogeneous Multi-Agent Systems Against Unbounded Attacks on Sensors and Actuators

Neuroadaptive Cooperative Fault-Tolerant Control of Heterogeneous Multiagent Systems Based on Fully Actuated System Approaches.

The leader-following cooperative problem in heterogeneous multiagent systems (HMASs) with unmodeled dynamics and actuator faults is investigated in this article. The HMASs, which include unmanned ground vehicles and unmanned aerial vehicles, are first described using a fully actuated system model (FASM). The FASM, as opposed to the first-order state-space model, preserves the physical significance of original systems and makes it feasible to apply the control rule entirely. In order to approximate unknown system dynamics, novel neuroadaptive laws with few learning parameters are then suggested. To counteract the negative effects of actuator faults, the Nussbaum function and adaptive approach are utilized. In addition, a cooperative fault-tolerant protocol is suggested, wherein consensus errors are uniformly ultimately bounded. The lack of virtual control variables in the proposed protocol reduces its complexity. The theoretical results are then validated by numerical simulations.

Adaptive Optimal Bipartite Consensus Control for Heterogeneous Multi-Agent Systems

Adaptive Optimal Bipartite Consensus Control for Heterogeneous Multi-Agent Systems

Finite-Time Hierarchical Containment Control of Heterogeneous Multi-agent Systems With Intermittent Communication

Finite-Time Hierarchical Containment Control of Heterogeneous Multi-agent Systems With Intermittent Communication

Formation Control of Heterogeneous Multi-Agent Systems Under Fixed and Switching Hierarchies

Formation Control of Heterogeneous Multi-Agent Systems Under Fixed and Switching Hierarchies

Adaptive Bipartite Output Containment Control of Heterogeneous Multi-agent Systems Based on Output Regulation Approach

Adaptive Bipartite Output Containment Control of Heterogeneous Multi-agent Systems Based on Output Regulation Approach

Dynamic Leader–Follower Output Containment Control of Heterogeneous Multiagent Systems Using Reinforcement Learning

Dynamic Leader–Follower Output Containment Control of Heterogeneous Multiagent Systems Using Reinforcement Learning

The bottleneck and ceiling effects in quantized tracking control of heterogeneous multi-agent systems under DoS attacks

In this paper, we investigate tracking control of heterogeneous multi-agent systems under Denial-of-Service (DoS) attacks and state quantization. Dynamic quantized mechanisms are designed for inter-follower communication and leader–follower communication. Zooming-in and out factors, and data rates of both mechanisms for preventing quantizer saturation are provided. Our results show that by tuning the inter-follower quantized controller, one cannot improve the resilience beyond a level determined by the data rate of leader–follower quantized communication, i.e., the ceiling effect. Otherwise, overflow of followers’ state quantizer can occur. On the other hand, if one selects a “large” data rate for leader–follower quantized communication, then the inter-follower quantized communication determines the resilience, and further increasing the data rate for leader–follower quantized communication cannot improve the resilience, i.e., the bottleneck effect. Simulation examples are provided to justify the results of our paper.