Event-triggered <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="mml4" display="inline" overflow="scroll" altimg="si4.gif"><mml:msub><mml:mrow><mml:mi mathvariant="script">H</mml:mi></mml:mrow><mml:mrow><mml:mi>∞</mml:mi></mml:mrow></mml:msub></mml:math> state estimation for semi-Markov jumping discrete-time neural networks with quantization

Abstract:In recent years, the wind energy conversion system (WECS) has been becoming the vital system to acquire wind energy. However, the high failure rate of WECSs leads to expensive costs for the maintenance of WECSs. Therefore, how to detect and isolate the faults of WECSs with stochastic dynamics is the pressing issue in the literature. This paper proposes a novel comprehensive fault detection and isolation (FDI) method for WECSs. First, a stochastic model predictive control (SMPC) controller is studied to construct the closed-loop system of the WECS. This controller is based on the Markov-jump linear model, which could precisely establish the stochastic dynamics of the WECS. Meanwhile, the SMPC controller has satisfied control performance for the WECS. Second, based on the closed-loop system with SMPC, the stochastic hybrid estimator (SHE) is designed to estimate the continuous and discrete states of the WECS. Compared with the existing estimators for WECSs, the proposed estimator is more suitable for WECSs since it considers both the continuous and discrete states of WECSs. In addition, the proposed estimator is robust to the fault input. Finally, with the proposed estimator, the comprehensive FDI method is given to detect and isolate the actuators' faults of the WECS. Both the system status and the actuators' faults can be detected by the FDI method and it can effectively quantify the actuators' fault by the fault residuals. The simulation results suggest that the SHE could effectively estimate the hybrid states of the WECS, and the proposed FDI method gives satisfied fault detection performance for the actuators of the WECS.

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“…In this way, the SMPC's Exp performance index (problem (14)) is solved as a quadratic programming (QP) problem (19).…”

Section: Scenario-based Smpc Designmentioning

confidence: 99%

“…Scholars also have extensive and in-depth research on state estimation [14][15][16][17][18][19][20]. In the literature, the Kalman filter is the most commonly used method to estimate states [14].…”

Section: Introductionmentioning

confidence: 99%

Stochastic Hybrid Estimator Based Fault Detection and Isolation for Wind Energy Conversion Systems with Unknown Fault Inputs

et al. 2018

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“…e application areas of quantization technology are diverse under different network frames. For neural network, a logarithmic static and timeinvariant quantizer are considered in [25], and quantization is employed in discrete semi-Markov jump network in [26]. e effects of different logarithmic quantizers are investigated in [27], the so-called convex combination and sector bounded.…”

Section: Introductionmentioning

confidence: 99%

Hybrid-Driven Mechanism Based on Uncertain Network for Markov Jump System with Quantizations and Delay

et al. 2020

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is paper investigates the hybrid-driven mechanism problem for Markov jump system, where both channel quantization (BCQ) and network-induced delay based on uncertain network are considered. Firstly, comparing with the traditional event-triggered scheme, a hybrid-driven mechanism is employed in networked control systems (NCSs) for the finite capacity of communication bandwidth resources and system performance in equilibrium. en, the quantization technology is applied in the communication channel from sensor-to-controller and controller-to-actuator. e application of BCQ is for further investigation that mitigate data packet transmission rate. irdly, Markov jump system is modeled for the hybrid-driven mechanism and network-induced delay. By constructing the Lyapunov-Krasovskii function, a sufficient condition is derived as the stability criterion, and the controller is designed in which the nonlinear term is rewritten for simplifying the calculation. Finally, two simulation examples are provided to demonstrate the effectiveness of the proposed approach.

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“…Nevertheless, Markovian jump systems have limited applications since the sojourn time of Markov chain is exponentially distributed . Thus, in recent years, a more general kind of a hybrid system called the semi‐Markovian jump system whose probability distribution is arbitrary has attracted great attention . In the works of Pradeepa et al and Rakkiyappan et al, the same dynamic ETS (DETS) as in the work of Wang et al is also employed to solve relevant issues on semi‐Markovian jump systems.…”

Section: Introductionmentioning

confidence: 99%

“…14,15 Thus, in recent years, a more general kind of a hybrid system called the semi-Markovian jump system whose probability distribution is arbitrary has attracted great attention. 4,16,17 In the works of Pradeepa et al and Rakkiyappan et al,4,17 the same dynamic ETS (DETS) as in the work of Wang et al 12 is also employed to solve relevant issues on semi-Markovian jump systems.It is noted that the traditional DETS used in the aforementioned literature is in a popular form as (is the newly sampled data to be detected, and the measurement error is given asẽ k (t) = x(t k h)−x(t k h + lh). Recently, with the rapid increasing attention upon event-triggered control, several new and improved ETSs are proposed, such as mixed ETSs, integral ETSs, and so on.…”

mentioning

confidence: 99%

H_∞ stabilization for networked semi‐Markovian jump systems with randomly occurring uncertainties via improved dynamic event‐triggered scheme

2019

Intl J Robust & Nonlinear

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Summary This paper aims to solve the H∞ stabilization problem for networked semi‐Markovian jump systems subject to randomly occurring uncertainties by an improved event‐triggered technique. A new measurement error that is defined as the difference value between the latest transmitted data and the mean value of both current data and latest transmitted data is introduced into the event‐triggered condition. Compared with traditional dynamic event‐triggered scheme, more unexpected data could be avoided to be transmitted, which is demonstrated in the simulation through sufficient comparison experiments. Furthermore, by employing a Lyapunov‐Krasovskii functional method and a free‐weighting matrix method, sufficient conditions are derived to guarantee the stabilization of the closed‐loop semi‐Markovian jump time‐delay system with uncertainties and a prescribed performance index. Then, a codesign method for H∞ controller gains and event‐triggered parameters is presented. Finally, simulations are given to verify the effectiveness of our improved dynamic event‐triggered scheme.

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Event-triggered H∞ state estimation for semi-Markov jumping discrete-time neural networks with quantization

Cited by 59 publications

References 57 publications

Stochastic Hybrid Estimator Based Fault Detection and Isolation for Wind Energy Conversion Systems with Unknown Fault Inputs

Stochastic Hybrid Estimator Based Fault Detection and Isolation for Wind Energy Conversion Systems with Unknown Fault Inputs

Hybrid-Driven Mechanism Based on Uncertain Network for Markov Jump System with Quantizations and Delay

H_∞ stabilization for networked semi‐Markovian jump systems with randomly occurring uncertainties via improved dynamic event‐triggered scheme

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Event-triggered H∞ state estimation for semi-Markov jumping discrete-time neural networks with quantization

Cited by 59 publications

References 57 publications

Stochastic Hybrid Estimator Based Fault Detection and Isolation for Wind Energy Conversion Systems with Unknown Fault Inputs

Stochastic Hybrid Estimator Based Fault Detection and Isolation for Wind Energy Conversion Systems with Unknown Fault Inputs

Hybrid-Driven Mechanism Based on Uncertain Network for Markov Jump System with Quantizations and Delay

H∞ stabilization for networked semi‐Markovian jump systems with randomly occurring uncertainties via improved dynamic event‐triggered scheme

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H_∞ stabilization for networked semi‐Markovian jump systems with randomly occurring uncertainties via improved dynamic event‐triggered scheme