Consensus Control of Linear Parameter-Varying Multi-Agent Systems with Unknown Inputs

Zhu, Fanglai; Tan, Carol

doi:10.3390/s23115125

Cited by 5 publications

(3 citation statements)

References 41 publications

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“…Here, d ij is the distance between agent i and agent j. Equation (1) shows that if mutual detection is not possible, a connection cannot be established. This is in line with reality.…”

Section: Preliminary and Problem Formulationmentioning

confidence: 99%

“…The decision making and analysis of multi-agent behavior for controlling large-scale systems have come to the forefront of current research in the field of control, as these systems rely on agent communication to achieve the overall expected behavior. With the joint efforts of numerous researchers, MASs have made significant progress in research areas such as consensus [1,2], cluster [3], flocking/swarm [4,5], and hunting control. However, with the expansion of the application scope, some challenges have also arisen for practical engineering problems.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Multi-Target Self-Organization Hunting Control of Multi-Agent Systems

He,

Wang,

Liu

et al. 2024

Applied Sciences

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In this paper, we present a novel coordinated method tailored to address the dynamic multi-target hunting control problem in multi-agent systems, offering significant practical value. Our approach encompasses several key components: initially, we introduce a task allocation model that integrates a fuzzy inference system with a particle swarm optimization algorithm. This hybrid model efficiently allocates hunting tasks for scattered evading targets, effectively transforming the dynamic multi-target hunting problem into multiple dynamic single-target-hunting problems. This transformation enhances the speed and efficacy of task allocation. Subsequently, we propose an attraction/repulsive model grounded in potential field theory. This model facilitates the coordinated hunting of each target by organizing agents into subgroups. Relying solely on relative position and velocity information between agents and targets, our model simplifies computation, while maintaining effectiveness. Furthermore, the coordination of hunting activities for each target is achieved through a series of agent subgroups, guided by our proposed motion model. This systematic approach ensures a cohesive and efficient hunting strategy. Finally, we validate the effectiveness and feasibility of our proposed method through simulation results. These results provide empirical evidence of the method’s efficacy and potential applicability in real-world scenarios.

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“…Here, d ij is the distance between agent i and agent j. Equation (1) shows that if mutual detection is not possible, a connection cannot be established. This is in line with reality.…”

Section: Preliminary and Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic Multi-Target Self-Organization Hunting Control of Multi-Agent Systems

He,

Wang,

Liu

et al. 2024

Applied Sciences

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“…In the isolation stage, a bank of robust and non-linear UIOs were designed to isolate the faulty actuator, allowing the unknown input disturbances to be decoupled and their effects attenuated. In addition, UIOs can effectively track state errors in multi-agent systems with directed switching topologies, enabling consensus tracking, even with external inputs affecting the leader [33]. For example, the authors in [34] used a UIO to estimate an unknown state affected by simultaneous connectivity-mixed attacks, actuator/sensor faults, and disturbances, and developed a leader-follower consensus control for a MAS.…”

Section: Introductionmentioning

confidence: 99%

Consensus Tracking Control of Multiple Unmanned Aerial Vehicles Subject to Distinct Unknown Delays

Campos-Martínez,

Hernández-González,

Guerrero-Sánchez

et al. 2024

Machines

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This article deals with the consensus tracking problem for multi-agent systems (MAS) under the influence of unknown time-varying delays. Each agent of the MAS is a quadrotor unmanned aerial vehicle (UAV) represented as a linear continuous-time system. The main objective of this paper is the stabilization of multi-agent systems where the control input is affected by unknown time-varying delays, which are assumed to be upper-bounded, and where these bounds are not required to be known. The proposed observer-based control scheme guarantees the consensus tracking of multi-UAV systems with the desired H∞ performance, which adds a further level of mitigation of unknown delays present in MAS systems by minimizing the H∞ norm, which measures the maximum gain from the disturbance to the controlled output of the system. For each UAV agent, an unknown input observer is employed to isolate the unknown time-varying delays in the state estimation process. With the use of an unknown input observer-based consensus tracking control, sufficient conditions are derived to ensure that all follower UAVs can reach a consensus with the leader, despite the presence of distinct unknown time-varying delays. The stability of the proposed scheme is proven using Lyapunov theory for the leader and follower agents. Finally, numerical examples are provided to illustrate the effectiveness of the proposed method.

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Observer‐based event‐triggered consensus control for multi‐agent systems with nonlinearity and unknown inputs

Weng,

Zhu

2023

Intl J Robust & Nonlinear

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This article investigates observer‐based consensus issues for a class of Lipschitz nonlinear multi‐agent systems (MASs) with unknown input. To address the challenges because of the unmeasurable states and the unknown input of each agent system, an interval observer is utilized to design an unknown input observer (UIO). The UIO possesses the capability of generating asymptotically the estimations of both the system states and unknown input simultaneously. Subsequently, a distributed control protocol scheme is put forwarded. The distributed controller for each agent is designed by using the state estimation and the unknown input reconstruction (UIR). The unknown input is compensated thanks for the introduction of the UIR into the controller, and thus leads to a better control performance. Besides, the event‐triggered strategy is also considered such that agents communicate with each other discontinuously. In this way, an observer‐based event‐triggered distributed control protocol scheme is developed, and under the scheme, the asymptotic convergent MASs consensus is accomplished. Finally, the effectiveness of the proposed methods is verified by a simulation example.

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Consensus Control of Linear Parameter-Varying Multi-Agent Systems with Unknown Inputs

Cited by 5 publications

References 41 publications

Dynamic Multi-Target Self-Organization Hunting Control of Multi-Agent Systems

Dynamic Multi-Target Self-Organization Hunting Control of Multi-Agent Systems

Consensus Tracking Control of Multiple Unmanned Aerial Vehicles Subject to Distinct Unknown Delays

Observer‐based event‐triggered consensus control for multi‐agent systems with nonlinearity and unknown inputs

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