<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.gif" overflow="scroll"><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mo>∞</mml:mo></mml:mrow></mml:msub></mml:math>filtering for networked linear systems with multiple packet dropouts and random delays

Li, Xiuying; Sun, Shuli

doi:10.1016/j.dsp.2015.07.008

Cited by 26 publications

(8 citation statements)

References 33 publications

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“…Example 1: The following F-404 aircraft engine system described in Li and Sun (2015) with parameter matrices is adopted as…”

Section: Illustrative Examplesmentioning

confidence: 99%

Fault estimation of linear discrete time-varying systems with multiplicative noise based on finite impulse response filter

Liu

et al. 2019

Transactions of the Institute of Measurement and Control

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This paper aims to construct a finite impulse response (FIR) based fault estimator for a class of linear discrete time-varying systems (LDTV) with multiplicative noise. Drawing support of intensive stochastic analyses and matrix manipulations, a novel performance index is proposed such that the fault estimation error is minimized in stochastic sense. A necessary and sufficient condition is established to guarantee the existence of the FIR-based fault estimator with satisfied estimation accuracy. The optimal gain of the desired fault estimator is calculated in an analytical way by minimizing the aforementioned performance index. Several examples are presented to demonstrate the effectiveness and superiority of the proposed methods.

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“…Example 1: The following F-404 aircraft engine system described in Li and Sun (2015) with parameter matrices is adopted as…”

Section: Illustrative Examplesmentioning

confidence: 99%

Fault estimation of linear discrete time-varying systems with multiplicative noise based on finite impulse response filter

Liu

et al. 2019

Transactions of the Institute of Measurement and Control

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“…In Geromel and Gabriel (2015), Li and Sun (2015), Morais and Peres (2015) and Wang et al (2010c), disturbances were considered in NCSs, and the robust H 2 and H∞ control problems are studied. In Lu et al (2016), for NCSs with random packet loss and uncertainties, a novel state space model was introduced and an improved model predictive tracking control was designed.…”

Section: Ncss With Uncertainties and Disturbancementioning

confidence: 99%

Challenging research for networked control systems: A survey

Meng

Chen

2018

Transactions of the Institute of Measurement and Control

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Nowadays, networked control systems (NCSs) have been a popular research topic in both academia and industry areas. However, the NCSs also present great challenging problems due to imperfect communication, node moving, external interference and hacker attack. In this paper, we review some of the recent challenging researches for NCSs. Several concerned research subjects in NCSs are surveyed, including network-induced delays, packet dropouts, packet disordering, external disturbance, parameter uncertainty, data quantization, event-triggers mechanism, sensor or actuator failures, scheduling protocols, network attack and network topology. Correspondingly, various control laws to deal with these issues are discussed. Finally, some conclusions are given, and possible future directions are explored.

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“…[1][2][3][4] However, packet dropout is a vital problem for NCSs because of the limited resources for communication networks. [1][2][3]5] To tackle the packet dropouts of NCSs, the H ∞ control method is proposed, [6] but the uncertainties of packet losses are often ignored. A quantized control algorithm has been used considering the characteristics of packet dropouts, [7] but the packet losses from controller to actuator are not involved.…”

Section: Introductionmentioning

confidence: 99%

A Cognitive Control Approach for Networked Control Systems with Random Packet Losses

Wang

Yin

et al. 2021

Advcd Theory and Sims

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This paper presents a novel hybrid cognitive control method inspired by human brain mechanism for networked control systems (NCSs) with random packet dropouts. The random packet losses are assumed to occur in both channels from sensor to controller and from controller to actuator. Different from most of the existing studies, the information entropy is utilized to characterize the uncertainties of the random packet dropouts. A hybrid cognitive control model is designed for NCSs subject to random packet losses. More specifically, the proposed hybrid cognitive control algorithm is constructed by using the Q-learning and backstepping control simultaneously. That is, the Q-learning is employed for the cognitive controller to regulate the backstepping controller and meantime act on the control object. In addition, the information gap is defined based on the information entropy, and used for depicting the uncertainties of the random packet dropouts. Simulation results are performed to validate the designed hybrid cognitive control method, demonstrating that it can eliminate the effects of packet losses and thus performs better than the traditional backstepping or proportional-integralderivative (PID) controllers.

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H∞filtering for networked linear systems with multiple packet dropouts and random delays

Cited by 26 publications

References 33 publications

Fault estimation of linear discrete time-varying systems with multiplicative noise based on finite impulse response filter

Fault estimation of linear discrete time-varying systems with multiplicative noise based on finite impulse response filter

Challenging research for networked control systems: A survey

A Cognitive Control Approach for Networked Control Systems with Random Packet Losses

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