Containment control of non-affine multi-agent systems based on given precision

Yao, Dajie; Dou, Chunxia; Xie, Xiangpeng; Hu, Songlin

doi:10.1016/j.amc.2021.126579

Cited by 7 publications

(2 citation statements)

References 42 publications

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“…In recent containment studies [25][26][27], the main approach to achieve containment control involves transforming the leaders' information into a set of tracking signals within the convex hull using Laplace matrix transformation. The number of tracking signals corresponds to the number of followers.…”

Section: Introductionmentioning

confidence: 99%

Event-triggered based adjustable containment control scheme design for multi-agent systems

Jiang,

Yang,

Gao

et al. 2024

Eng. Res. Express

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The traditional containment control problem uses a mapping method with a fixed communication topology to generate a set of reference signals for followers, which may lead to a situation where the position of the reference signal cannot be adjusted and causes collisions. Therefore, based on backstepping theory, we propose a novel containment control scheme to address this problem, where the generated reference signals can be adjusted arbitrarily. In particular, it is important to point out that an estimator is designed to guide the follower if the reference signal is not available, so that the collision problem is avoided. In addition, the computational complexity of the controller is also reduced and the communication resources are saved by introducing the command filters and event-triggered mechanism. It is proved that the tracking errors of the closed multi-agent systems are uniformly and ultimately bounded through Lyapunov theory. Finally, experimental simulations verify that the proposed control scheme is reasonable and effective. Furthermore, the proposed scheme provides a more flexible solution for containment control studies, especially for collision avoidance research of multi-agents.

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Section: Introductionmentioning

confidence: 99%

Event-triggered based adjustable containment control scheme design for multi-agent systems

Jiang,

Yang,

Gao

et al. 2024

Eng. Res. Express

View full text Add to dashboard Cite

show abstract

“…Compared with the previous works (Shahvali et al, 2020; Yao et al, 2022; Yuan et al, 2019), the event-triggered adaptive neural network backstepping technology is proposed for the FOMASs containment control problem. Furthermore, the considered MAS contains unmeasured states and system nonlinearities.…”

Section: Introductionmentioning

confidence: 99%

Command-filtered adaptive containment control of fractional-order multi-agent systems via event-triggered mechanism

Chen

Yuan

2022

Transactions of the Institute of Measurement and Control

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In this paper, the containment control problem of a class of fractional-order nonlinear multi-agent systems is studied, in which the multi-agent system contains unmeasured states and system nonlinearity. An adaptive neural network backstepping controller combined with event-triggered mechanism is proposed to ensure all followers can converge to the convex hull spanned by the leaders. The command filter is introduced into the proposed fractional-order control system to obtain fractional derivatives for virtual control functions continuously and avoid “explosion of complexity.” From the Lyapunov stability theory, all the signals can remain semi-global uniformly ultimately bounded in the closed-loop system. Numerical example and simulation results confirm the feasibility of the proposed control method.

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Quantized iterative learning control for nonlinear multi-agent systems with initial state error

Zhang,

Li,

Chen

2024

Systems & Control Letters

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Containment control of non-affine multi-agent systems based on given precision

Cited by 7 publications

References 42 publications

Event-triggered based adjustable containment control scheme design for multi-agent systems

Event-triggered based adjustable containment control scheme design for multi-agent systems

Command-filtered adaptive containment control of fractional-order multi-agent systems via event-triggered mechanism

Quantized iterative learning control for nonlinear multi-agent systems with initial state error

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