Event-triggered adaptive sliding mode control for consensus of multiagent systems with unknown disturbances

Chen, Tianxing; Zhuang, Xuebin; Hou, Zhengxin; Chen, Hongbo

doi:10.1038/s41598-022-21704-4

Cited by 10 publications

(7 citation statements)

References 46 publications

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“…λ ji represents the adaptive rate of the j uncertain parameter of the i generator in Eq (26) [36][37][38].…”

Section: Plos Onementioning

confidence: 99%

Research on robust adaptive control of strong nonlinear complex large power grids

Huang,

Chen,

Zhang

et al. 2024

PLoS ONE

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In this paper, a Multi-Index Nonlinear Robust Adaptive Control (MINRAC) method was proposed for ultra-complex multi-input multi-output power systems with uncertain parameter factors and external disturbances. The controller designed by this method has excellent static and dynamic characteristics. Under the condition of the uncertainty of system parameters and external disturbance at the same time, the MINRAC method can ensure that the multiple indexes concerned by ultra-complex power systems can be controlled at their expected values. The simulation results showed that the control mechanism of MINRAC method was consistent in both single-machine infinite bus system and multi-machine interconnected coupling system. The output function chooses power angle, angular frequency and terminal voltage as constraints. When the system has parameter uncertainty and external interference, the uncertain parameter values are adjusted by adaptive control to force these indicators to tend to the given expected value. For three-phase short circuit, which is the most serious fault in power system, the use of multi index nonlinear robust controller can ensure that the system is stable in a wide range and has better dynamic performance.

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“…λ ji represents the adaptive rate of the j uncertain parameter of the i generator in Eq (26) [36][37][38].…”

Section: Plos Onementioning

confidence: 99%

Research on robust adaptive control of strong nonlinear complex large power grids

Huang,

Chen,

Zhang

et al. 2024

PLoS ONE

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show abstract

“…When the neural network is applied to information transmission, it is necessary to sample the data from time to time, although it is possible to transmit all the data resources, sometimes unnecessary data is also transmitted, which can waste the communication resources.But event-triggered control can increase efficiency while guaranteeing performance 30 , 31 . Most of the studies on controlling the periodic execution of events for signals in communication transmission systems has been through ZOH or time-event-triggered schemes 32 , 33 .…”

Section: Introductionmentioning

confidence: 99%

“…The stability theory proposed by Lyapunov in 1892 is highly superior in the study of stability 25 : for internal descriptive models; for univariate, linear, constant; for multivariate, nonlinear, time-varying systems. Lyapunov functional method is a simple and useful method to study stability of neural networks with uncertainties [26][27][28][29] .When the neural network is applied to information transmission, it is necessary to sample the data from time to time, although it is possible to transmit all the data resources, sometimes unnecessary data is also transmitted, which can waste the communication resources.But event-triggered control can increase efficiency while guaranteeing performance 30,31 . Most of the studies on controlling the periodic execution of events for signals in communication transmission systems has been through ZOH or time-event-triggered schemes 32,33 .…”

mentioning

confidence: 99%

H∞ state estimation of continuous-time neural networks with uncertainties

Li,

Chen,

et al. 2024

Sci Rep

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Abstract$$H_{\infty }$$ H ∞ state estimation is addressed for continuous-time neural networks in the paper. The norm-bounded uncertainties are considered in communication neural networks. For the considered neural networks with uncertainties, a reduced-order $$H_{\infty }$$ H ∞ state estimator is designed, which makes that the error dynamics is exponentially stable and has weighted $$H_{\infty }$$ H ∞ performance index by Lyapunov function method. Moreover, it is also given the devised method of the reduced-order $$H_{\infty }$$ H ∞ state estimator. Then, considering that sampling the output y(t) of the neural network at every moment will result in waste of excess resources, the event-triggered sampling strategy is used to solve the oversampling problem. In addition, a devised method is also given for the event-triggered reduced-order $$H_{\infty }$$ H ∞ state estimator. Finally, by the well-known Tunnel Diode Circuit example, it shows that a lower order state estimator can be designed under the premise of maintaining the same weighted $$H_{\infty }$$ H ∞ performance index, and using the event-triggered sampling method can reduce the computational and time costs and save communication resources.

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“…Su 20 proposed an integral sliding mode manifold and its TSMC, and manifested the global finite-time convergence of both sliding mode manifold and tracking error. The above SMC provide an effective and stable control strategy for nonlinear systems, but these rely on the prior knowledge of system uncertainties 21 – 24 . Besides, these SMC trajectory tracking control of the robotic manipulator only ensures the tracking accuracy of each joint, but the accuracy of contour error is not guaranteed 25 , 26 .…”

Section: Introductionmentioning

confidence: 99%

Adaptive nonsingular terminal sliding mode control of robot manipulator based on contour error compensation

Zhu

Huang

et al. 2023

Sci Rep

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To achieve accurate contour tracking of robotic manipulators with system uncertainties, external disturbance and actuator faults, a cross-coupling contour adaptive nonsingular terminal sliding mode control (CCCANTSMC) is proposed. A nonsingular terminal sliding mode manifold is developed which eliminates the singularity completely. In order to avoid the demand of the prior knowledge of system uncertainties, external disturbance and actuator faults in practical applications, an adaptive tuning approach is proposed. The stability of the proposed control strategy is demonstrated by the finite-time stability theory. Then, the developed controller combines adaptive nonlinear terminal sliding mode control (ANTSMC) of joint trajectory tracking and proportion–differentiation control of end-effector contour tracking by introducing the coupling factor between multiple axes based on Jacobian. Moreover, a unified framework of cross-coupling contour compensation and reference position pre-compensation is built. Finally, numerical simulation and experimental results validate the effectiveness of the proposed control strategy.

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Event-triggered adaptive sliding mode control for consensus of multiagent systems with unknown disturbances

Cited by 10 publications

References 46 publications

Research on robust adaptive control of strong nonlinear complex large power grids

Research on robust adaptive control of strong nonlinear complex large power grids

H∞ state estimation of continuous-time neural networks with uncertainties

Adaptive nonsingular terminal sliding mode control of robot manipulator based on contour error compensation

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