Observer-Based Dynamic Event-Triggered Control for Multiagent Systems With Time-Varying Delay

Cao, Liang; Pan, Yingnan; Liang, Hongjing; Huang, Tingwen

doi:10.1109/tcyb.2022.3226873

Cited by 150 publications

(59 citation statements)

References 51 publications

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“…Remark Different from the results in References 34‐41, the ST fuzzy adaptive prescribed‐time tracking control problem is considered for stochastic nonlinear systems in this paper. In addition, compared with Reference 37, the control method designed in this paper can eliminate the need for continuous supervision and the settling time can be given in advance.…”

Section: Controller Design and Stability Analysismentioning

confidence: 93%

Self‐triggered adaptive prescribed‐time tracking control for stochastic nonlinear systems

Zhu

Pan

et al. 2023

Adaptive Control & Signal

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Summary This paper proposes a self‐triggered (ST) adaptive prescribed‐time tracking control method for a class of stochastic nonlinear systems. Different from the existing results, an improved ST mechanism is proposed by adding a judgment condition to reduce the negative effect of excessive design interval on system performance. Based on the one‐to‐one mapping and backstepping technique, an adaptive prescribed‐time tracking control method is proposed, which can make the error converge to the predefined precision set within the predetermined time. Simultaneously, applying the Lyapunov stability method, the boundedness of all signals in the closed‐loop system can be ensured. Finally, a detailed simulation example is provided to show the effectiveness of the proposed control strategy.

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Section: Controller Design and Stability Analysismentioning

confidence: 93%

Self‐triggered adaptive prescribed‐time tracking control for stochastic nonlinear systems

Zhu

Pan

et al. 2023

Adaptive Control & Signal

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“…It can be deduced from the lyapunov function (36) that z 1 , z 2 , z 3 , z 4 , θ 2 and θ 4 is uniformly bounded and converge to a small neighborhood of the origin [36], [37]. From the above desin process, it means that all other signals of the close-loop system x 1 , x 2 , x 3 , x 4 , e 1 , e 2 , e 3 , e 4 , ξ 1 and ξ 2 are bounded and converge to a small neighborhood of the origin.…”

Section: Stablization Analysismentioning

confidence: 99%

“…FIGURE 7.Comparison simulation results with 2-DIA of[36] under system parameters P1 under initial state IS1…”

mentioning

confidence: 99%

Adaptive Neural Network Command Filtered Backstepping Control for the Underactuated TORA System

Guo

Liu

2023

IEEE Access

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An adaptive neural network command-filtered backstepping design algorithm is proposed for the underactuated translational oscillator with a rotating actuator (TORA). The system dynamics are transformed into a nonlinear cascade system through a global change of coordinates. Considering the weak sinusoid-type nonlinear interaction and affine-free appearance in the cascade model, the TORA is looked at as a pure feedback system. Two neural networks are used to approximate the unknown functions and a command filter is used to produce the virtual control and its first and second-order derivatives to overcome the explosion of complexity problems in backstepping. A filter error compensation dynamic system is designed to overcome the error influence on control performance. Taking full account of the cascade model structure of the underactuated TORA, each backstepping step is designed for a second-order subsystem to reduce the design steps and simplify the design process. The system stability is proved through the Lyapunov stability theorem and verified by numerical simulations.

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“…Multi-agent systems (MASs) have been recognized and concerned by many scholars in recent years in view of their wide availability in power systems, 1 unmanned aerial vehicle (UAV) systems, 2,3 spacecraft formation flying, 4,5 and so forth. Therefore, more and more researches mainly involve different issues of MASs, such as consensus, [6][7][8][9] formation, [10][11][12] and leader-following, 13,14 and so forth.…”

Section: Introductionmentioning

confidence: 99%

Adaptive containment control of nonlinear multi‐agent systems about privacy preservation with multiple attacks

Gong

Cao

Pan

et al. 2023

Intl J Robust & Nonlinear

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Summary This paper focuses on the problem of adaptive containment control about privacy preservation in nonlinear multi‐agent systems, where followers suffer from multiple cyber‐attacks and leaders have non‐zero control inputs. A multiple attacks mold consisting of denial‐of‐service (DoS) attacks and false data‐injection attacks is constructed for nonlinear systems. The containment control protocol is updated by applying an event‐triggered scheme. This scheme is proposed to dispatch the updating information in the presence of multiple attacks. Meanwhile, to deal with the information insecurity that may exist in the control process, the protocol for privacy preservation about initial information is developed by introducing time‐varying functions as masks. Tracking errors of agents are described by taking the inputs of leaders as the external disturbances. This method does not depend on the widths of boundary layers and reduce the computational complexity. The stability of multi‐agent systems is analyzed by applying the Cauchy inequality, the Young's inequality and the Lyapunov function theory. Finally, two simulation examples are shown to illustrate the effectiveness of the proposed control strategy.

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Observer-Based Dynamic Event-Triggered Control for Multiagent Systems With Time-Varying Delay

Cited by 150 publications

References 51 publications

Self‐triggered adaptive prescribed‐time tracking control for stochastic nonlinear systems

Self‐triggered adaptive prescribed‐time tracking control for stochastic nonlinear systems

Adaptive Neural Network Command Filtered Backstepping Control for the Underactuated TORA System

Adaptive containment control of nonlinear multi‐agent systems about privacy preservation with multiple attacks

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