Adaptive NN-Based Event-Triggered Containment Control for Unknown Nonlinear Networked Systems

Zheng, Yukan; Li, Yuan‐Xin; Che, Wei‐Wei; Hou, Zhongsheng

doi:10.1109/tnnls.2021.3107623

Cited by 16 publications

(2 citation statements)

References 42 publications

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“…Remark 5. The universal approximation theorem, which is often used when handling approximation problems with NNs, may ensure Lemma 2 [32][33][34][35][36]. NNs approximation of unknown smooth function h i (x i , τ) is depicted in Figure 1.…”

Section: Lemma 2 ([39]mentioning

confidence: 99%

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Dynamic Event-Triggered Consensus Control for Markovian Switched Multi-Agent Systems: A Hybrid Neuroadaptive Method

et al. 2023

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This paper presents a solution to the consensus problem for a particular category of uncertain switched multi-agent systems (MASs). In these systems, the communication topologies between agents and the system dynamics are governed by a time-homogeneous Markovian chain in a stochastic manner. To address this issue, we propose a novel neuroadaptive distributed dynamic event-triggered control (DETC) strategy. By leveraging stochastic Lyapunov theory and matrix inequality methodology, we establish sufficient conditions for practical ultimate mean square consensus (UMSBC) of MASs using a combination of neural networks (NNs) adaptive control strategy and DETC method. Our approach employs a distributed adaptive NNs DETC mechanism in MASs with unknown nonlinear dynamics and upgrades it at the moment of event sampling in an aperiodic manner, resulting in significant savings in computation and resources. We also exclude the Zeno phenomenon. Finally, we provide numerical examples to demonstrate the feasibility of our proposed approach, which outperforms existing approaches.

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Section: Lemma 2 ([39]mentioning

confidence: 99%

“…In this paper, the consistency problem of a class of MASs with unknown nonlinearities is handled by employing a neural adaptive DET strategy. Compared with continuous-time neuroadaptive consensus control algorithms [32][33][34]36], the proposed consensus control rule not only accomplishes consensus control but also successfully reduces communication.…”

mentioning

confidence: 99%

Dynamic Event-Triggered Consensus Control for Markovian Switched Multi-Agent Systems: A Hybrid Neuroadaptive Method

et al. 2023

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Adaptive fault‐tolerant control for air cushion vehicle with fixed‐time convergence

Dan,

Mingyu,

Hanbo

et al. 2024

Asian Journal of Control

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This paper proposes a fixed‐time convergence adaptive sliding mode fault‐tolerant controller (ASFTC) to address the air cushion vehicle (ACV) trajectory tracking problem under unknown environmental disturbances and actuator faults. The introduced method enhances the robustness and reduces the chattering of the controller, by proposing an initial state‐independent fixed‐time convergence method combined with a global sliding mode surface which has the advantage of quickly reaching the “sliding mode”. The model knowledge neural network (MKNN) method is employed to eliminate uncertain parameter effects, and it adjusts disturbance and fault estimates in real time based on tracking errors without the need for upper‐bound disturbance information and additional observer compensation. Finally, simulations validate the effectiveness of the proposed adaptive fault‐tolerant control system.

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Neural Adaptive Dynamic Event-Triggered Containment Control for Uncertain Multi-Agent Systems Under Markovian Switching Dynamics

Cai,

Wu,

et al. 2024

Cogn Comput

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Adaptive NN-Based Event-Triggered Containment Control for Unknown Nonlinear Networked Systems

Cited by 16 publications

References 42 publications

Dynamic Event-Triggered Consensus Control for Markovian Switched Multi-Agent Systems: A Hybrid Neuroadaptive Method

Dynamic Event-Triggered Consensus Control for Markovian Switched Multi-Agent Systems: A Hybrid Neuroadaptive Method

Adaptive fault‐tolerant control for air cushion vehicle with fixed‐time convergence

Neural Adaptive Dynamic Event-Triggered Containment Control for Uncertain Multi-Agent Systems Under Markovian Switching Dynamics

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