A New Method for Fault Tolerant Control through Q-Learning

Hua, Changsheng; Ding, Steven X.; Shardt, Yuri A.W.

doi:10.1016/j.ifacol.2018.09.526

Cited by 12 publications

(3 citation statements)

References 6 publications

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“…In the model-based method, a precise model of the existing system should be known a priori, and fault identification is required no construct a postfault system model before active FTCS controller design. In the data-driven method, unlike in the model-based one, the system model is identified using available historical data [1].…”

Section: Fault-tolerant Control (Ftc) Strategymentioning

confidence: 99%

“…To avoid production deteriorations and enhanced system reliability, measures must be taken to stop the propagation of fault and restore the system as much as possible to satisfactory performance when the fault occurs. This practical requirement gives rise to lots of studies in Fault-Tolerant Control (FTC) from both industry and academia [1].…”

Section: Introductionmentioning

confidence: 99%

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A Q-Learning based Fault-Tolerant Controller with Application to CSTH System

Hosseini¹,

Salahshoor²

2021

Software Engineering and Applications

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Systems are continually subjected to faults or malfunctions because of age or sudden events, which might degrade the operation performance and even result in operation failure that is a quite important issue in safety-critical systems. Thus, this important problem is the main reason to use the Fault-Tolerant strategy to improve the system’s performance with the presence of faults. A fascinating property in Fault-Tolerant Controllers (FTCs) is adaptability to system changes as they evolve throughout system operations. In this paper, a Q-learning algorithm with a greedy policy was used to realize the FTC adaptability. Then, some fault scenarios are introduced in a Continuous Stirred Tank Heater (CSTH) to compare the closed-loop performance of the developed Q-learning-based FTC with concerning conventional PID controller and an RL-based FTC. The obtained results show the effectiveness of Q-learningbased FTC in different fault scenarios.

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Section: Fault-tolerant Control (Ftc) Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Q-Learning based Fault-Tolerant Controller with Application to CSTH System

Hosseini¹,

Salahshoor²

2021

Software Engineering and Applications

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“…The method is applied to a drag racing vehicle control based on a set of observers parametrized with Q , where each observer responds to one or more faults. In Hua, Ding, and Shardt (2018) a fault tolerant control structure is proposed without requiring any model information or identification procedure. It is applied in weakly stochastic environments using a learning of the YK-parameter Q , the effectiveness of the proposed method is demonstrated in simulation of a DC motor.…”

Section: Applicationsmentioning

confidence: 99%

Advances in Youla-Kucera parametrization: A Review

Mahtout

Navas

Milanés

et al. 2020

Annual Reviews in Control

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Youla-Kucera (YK) parametrization was formulated decades ago for obtaining the set of controllers stabilizing a linear plant. This fundamental result of control theory has been used to develop theoretical tools solving many control problems ranging from stable controller switching, closed-loop identification, robust control, disturbance rejection, adaptive control to fault tolerant control. This paper collects the recent work and classifies them according to the use of YK parametrization, Dual YK parametrization or both, providing the latest advances with main applications in different control fields. A final discussion gives some insights on the future trends in the field.

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A Reinforcement Learning-Based Fault Tolerant Control Design Approach Using the Double Q-Learning Algorithm

Hosseini,

Salahshoor

2024

Communications in Computer and Information Science

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A New Method for Fault Tolerant Control through Q-Learning

Cited by 12 publications

References 6 publications

A Q-Learning based Fault-Tolerant Controller with Application to CSTH System

A Q-Learning based Fault-Tolerant Controller with Application to CSTH System

Advances in Youla-Kucera parametrization: A Review

A Reinforcement Learning-Based Fault Tolerant Control Design Approach Using the Double Q-Learning Algorithm

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