Quantized Fault-Tolerant Control for Descriptor Systems with Intermittent Actuator Faults, Randomly Occurring Sensor Non-Linearity, and Missing Data

Kchaou, Mourad; Jerbi, Houssem; Ștefănoiu, Dan; Popescu, Dumitru

doi:10.3390/math10111872

Cited by 6 publications

(1 citation statement)

References 45 publications

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“…Methods to handle uncertainty and unobservability in degradation control were introduced in [41] for linear systems and discussed in [42] for non-linear and singular systems. Moreover, a method for degradation control of systems with non-linearities and missing data was suggested in [43].…”

Section: Introductionmentioning

confidence: 99%

Optimal Degradation-Aware Control Using Process-Controlled Sparse Bayesian Learning

Dadash,

Björsell

2023

Processes

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Efficient production planning hinges on reducing costs and maintaining output quality, with machine degradation management as a key factor. The traditional approaches to control this degradation face two main challenges: high costs associated with physical modeling and a lack of physical interpretability in machine learning methods. Addressing these issues, our study presents an innovative solution focused on controlling the degradation, a common cause of machine failure. We propose a method that integrates machine degradation as a virtual state within the system model, utilizing relevance vector machine-based identification designed in a way that offers physical interpretability. This integration maximizes the machine’s operational lifespan. Our approach merges a physical machine model with a physically interpretable data-driven degradation model, effectively tackling the challenges in physical degradation modeling and accessibility to the system disturbance model. By embedding degradation into the system’s state-space model, we simplify implementation and address stability issues. The results demonstrate that our method effectively controls degradation and significantly increases the machine’s mean time to failure. This represents a significant advancement in production planning, offering a cost-effective and interpretable method for managing machine degradation.

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Section: Introductionmentioning

confidence: 99%

Optimal Degradation-Aware Control Using Process-Controlled Sparse Bayesian Learning

Dadash,

Björsell

2023

Processes

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Event‐triggered anti‐disturbance control for switched singular systems based on the uncertainty and disturbance estimator

Yang,

2024

IET Control Theory & Appl

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This article studies the event‐triggered anti‐disturbance control problem for switched singular systems (SSSs) based on the uncertainty and disturbance estimator (UDE). First, a composite controller consisting of feedback control and anti‐disturbance control based on UDE is proposed. By constructing the multiple Lyapunov functions and using the average dwell time (ADT) method, sufficient conditions are given to ensure that the closed‐loop system is regular, impulse‐free and globally uniformly asymptotically stable (GUAS). Further, in order to save resources, the event‐triggered control method is introduced into the feedback control, and a novel hybrid event‐triggered mechanism (ETM) is proposed, which guarantees that asynchronous switching and Zeno behavior are excluded. And the solvable conditions in terms of linear matrix inequalities (LMIs) are given. Finally, the validity of the proposed results is verified by the simulations.

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Improved Event-Triggered Dynamic Output Feedback Control for Networked T–S Fuzzy Systems With Actuator Failure and Deception Attacks

Zhang

Zhao

Wang

et al. 2023

IEEE Trans. Cybern.

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Quantized Fault-Tolerant Control for Descriptor Systems with Intermittent Actuator Faults, Randomly Occurring Sensor Non-Linearity, and Missing Data

Cited by 6 publications

References 45 publications

Optimal Degradation-Aware Control Using Process-Controlled Sparse Bayesian Learning

Optimal Degradation-Aware Control Using Process-Controlled Sparse Bayesian Learning

Event‐triggered anti‐disturbance control for switched singular systems based on the uncertainty and disturbance estimator

Improved Event-Triggered Dynamic Output Feedback Control for Networked T–S Fuzzy Systems With Actuator Failure and Deception Attacks

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