Dynamic Event-Triggered Reinforcement Learning-Based Consensus Tracking of Nonlinear Multi-Agent Systems

Bo, Xu; Li, Yuan‐Xin; Hou, Zhongsheng; Ahn, Choon Ki

doi:10.1109/tcsi.2023.3246001

Cited by 18 publications

(10 citation statements)

References 48 publications

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“…The Takagi‐Sugeno fuzzy system serves as the basis for an artificial neural network known as an adaptive neural‐fuzzy inference system (ANFIS). The 1990s marked the beginning of widespread use of this technique [12, 13]. The capabilities of both neural networks and fuzzy logic can be utilized in this system.…”

Section: Suggested Methodsmentioning

confidence: 99%

“…Here, we provide a cost‐optimized PMU with a number of novel features to identify the cause of electrical problems and suggest potential fixes [11]. The work [12] proposes a model for estimating the dependability of PMUs and then expands on this model to look at different PMU hardware designs. The proposed model is analyzed using the Markov process, and the corresponding two‐state PMU model is presented.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Diagnosis and classification of disturbances in the power distribution network by phasor measurement unit based on fuzzy intelligent system

Khosravi,

Trik,

Ansari

2024

The Journal of Engineering

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The dynamic nature of distribution networks raises fresh issues with how such electrical systems function. These networks have some characteristics that indicate the need for better monitoring and control capabilities, including dispersed generation employing renewable resources, changing load profiles, and rising reliability requirements. Phasor measurement units (PMUs) offer simultaneous voltage and current phasor measurements at various places and offer a variety of options for gauging the condition and health of the power distribution network. In this regard, a cost‐optimized PMU with some unique features for distribution systems is presented in this work. These features include a fuzzy inference system to determine the root cause of potential electrical disturbances and methods to estimate electrical parameters through measured field data, which are necessities. This study takes into account the modelling of PMUs, utilizing a process for fault detection and classification with a fuzzy inference network. The 9‐bus distribution network's dependability model is built once the components and their functions are first outlined. The proposed model is then used to calculate the availability of the presented model, which has been examined to provide an analogous reliability model for PMUs. Depending on the specific manufacturer, the PMU's design and specs will change. To extract phase and size measurement features for the proposed model adaptive neural‐fuzzy inference system network's training, two PMU structures and associated reliability models are described here. When merging input data for fuzzy neural network prediction using MATLAB software, fuzzy sets are taken into account for error classification analysis.

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Section: Suggested Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Diagnosis and classification of disturbances in the power distribution network by phasor measurement unit based on fuzzy intelligent system

Khosravi,

Trik,

Ansari

2024

The Journal of Engineering

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“…As an alternative, event-triggered control (ETC) executes control tasks only when satisfying the predesigned event-triggered conditions. [18][19][20] Therefore, such sampling behaviors are aperiodic and depend on the predesigned triggering information. 21,22 In comparison with the conventional periodic samplings, the ETC strategy is more desirable when limited bandwidth is encountered.…”

Section: Introductionmentioning

confidence: 99%

Adaptive dynamic surface control of MIMO nonlinear systems: A hybrid event triggering mechanism

Huang,

Zong,

et al. 2023

Adaptive Control & Signal

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SummaryThis paper addresses the event‐triggered stabilization control for multi‐input and multi‐output (MIMO) nonlinear systems. To handle the “explosion of complexity”, the dynamic surface control (DSC) technology is introduced to the proposed control scheme. In addition, the event‐driven idea is adopted to mitigate the data transmission pressure in sensor‐to‐controller and controller‐to‐actuator channels. Considering that the non‐derivable virtual controllers are commonly constructed in the event‐triggered control schemes aiming at saving dual‐channel resources, in this paper, a hybrid event‐triggering mechanism with multiple event detectors is developed to obtain differentiable virtual control signals, thereby providing a suitable application environment for the DSC technology. Meanwhile, an output‐based adaptive threshold is designed to obtain more communication benefits compared with the fixed threshold strategy. Finally, all signals in the closed‐loop system are ensured to be bounded under a standard Lyapunov stability analysis framework, and the effectiveness of the proposed control scheme is validated by a simulation example.

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“…In recent years, coordinated control of multi-agent systems (MASs) has gained widespread attention and has been widely used in the fields of formation control, 1 aggregation control, 2 and sensor networks. 3 As a fundamental problem of distributed coordination, consensus control for MASs is to design a distributed control protocol that enables each follower can track the trajectory of the leader. It is worth noting that the problem of consensus control of MASs in different situations has obtained many investigations in the last decade.…”

Section: Introductionmentioning

confidence: 99%

Event‐triggered distributed consensus control of nonlinear multi‐agent systems with unknown Bouc–Wen hysteresis input and DoS attacks

Guo,

Xu,

Niu

et al. 2023

Adaptive Control & Signal

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In this paper, the event‐triggered distributed consensus tracking control problem for a class of multi‐agent systems (MASs) is studied, where denial‐of‐service (DoS) attacks and Bouc‐Wen hysteresis inputs are considered in the communication channel and the actuator, respectively. First, when communication networks are subject to malicious DoS attacks, the connection weights of the communication topology graphs are affected. To address this problem, this paper establishes topological models of the communication network using a switching topology method, and gives connection recovery strategies for the communication network. Second, in order to save communication resources, a distributed control protocol based on an event‐triggered mechanism and locally deployed estimator is designed for DoS signals occurring in the communication network of states and control inputs. In addition, the effect of unknown Bouc–Wen hysteresis input is eliminated using the Nussbaum‐type function. Then, based on Lyapunov stability theory, the boundedness of all signals in the closed‐loop system is verified, and the objective of consensus control is achieved. Finally, the effectiveness of the proposed control scheme is demonstrated by numerical and practical simulation examples.

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Dynamic Event-Triggered Reinforcement Learning-Based Consensus Tracking of Nonlinear Multi-Agent Systems

Cited by 18 publications

References 48 publications

Diagnosis and classification of disturbances in the power distribution network by phasor measurement unit based on fuzzy intelligent system

Diagnosis and classification of disturbances in the power distribution network by phasor measurement unit based on fuzzy intelligent system

Adaptive dynamic surface control of MIMO nonlinear systems: A hybrid event triggering mechanism

Event‐triggered distributed consensus control of nonlinear multi‐agent systems with unknown Bouc–Wen hysteresis input and DoS attacks

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