Fault tolerant compensation strategy for stochastic distribution systems with communication constraints

Li, Lifan; Yao, Lina

doi:10.1016/j.jfranklin.2022.10.051

Cited by 3 publications

(1 citation statement)

References 36 publications

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“…In Yin et al [30], the PDF of the output estimation residual was modelled by the RBF neural network and the minimum entropy filtering algorithm was investigated on the basis of this model. SDC theory has also been implemented in industry, such as the paper-making process [31], refining process [29], and the grinding process [32]. In Zhang and Wang [33], a novel PDF model was proposed and applied to the twin tank level process, in which the probability density state vector was constructed by kernel density estimation and interpolation.…”

Section: Introductionmentioning

confidence: 99%

Fault‐tolerant control design for stochastic distribution control systems based on k‐step fault estimation

Yuan,

Yao

2023

Asian Journal of Control

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A novel scheme of fault‐tolerant control (FTC) and fault estimation (FE) for the stochastic distribution control (SDC) system with disturbance is investigated. The output probability density function (PDF) is modelled by the weight vector of the B‐spline neural network with the given basis function, and control of output probability density function can be converted to control of the weight vector of the B‐spline neural network. A k‐step fault estimation algorithm aiming at enhancing the accuracy of fault estimation is first applied to the stochastic distribution control system. Subsequently, a dynamic output feedback fault‐tolerant control algorithm is constructed by using the k‐step fault estimation information to guarantee that the target weight vector can be still approximated by the output weight vector in case of actuator fault. A simulation example is used to confirm the availability of the presented method.

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