Fault diagnosis and fault-tolerant control for non-Gaussian nonlinear stochastic systems via entropy optimisation

Yao, Lina; Li, Lifan; Guan, Yacun; Wang, Hao

doi:10.1080/00207721.2019.1671535

Cited by 6 publications

(6 citation statements)

References 20 publications

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“…Based on minimum entropy, [50] designed an active fault tolerant control strategy for non-Gaussian stochastic distribution systems with mean constraints. [51] presented the non-Gaussian fault tolerant control (FTC) for singular stochastic distribution systems using TS fuzzy modelling techniques. Furthermore, [52] investigated the fault diagnosis for non-Gaussian stochastic systems via entropy optimisation.…”

Section: Fault Diagnosis For Non-gaussian Systemsmentioning

confidence: 99%

Recent Advances in Non-Gaussian Stochastic Systems Control Theory and Its Applications

Zhang

Zhou

2022

IJNDI

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Survey/review study Recent Advances in Non-Gaussian Stochastic Systems Control Theory and Its Applications Qichun Zhang 1, and Yuyang Zhou 2,* 1 Department of Computer Science, University of Bradford, Bradford BD7 1DP, United Kingdom 2 School of Computing, Engineering and the Built Environment, Edinburgh Napier University, Edinburgh EH10 5DT, United Kingdom * Correspondence: y.zhou@napier.ac.uk Received: 24 October 2022 Accepted: 18 November 2022 Published: 22 December 2022 Abstract: Non-Gaussian randomness widely exists in complex dynamical systems, in which the traditional mean-variance index cannot fully reflect the systematic characteristics. To improve the performance of control design subjected to non-Gaussian noises, stochastic distribution control (SDC) theory was proposed in the 1990s, where the output probability density function (PDF) has been investigated as an additional system variable. Following this framework, SDC has been extended to other research subjects in control systems such as filter design, fault diagnosis, and so on. It shows that SDC supplies an important solution to enhance the accuracy of system design, which is further beneficial to almost all the topics subject to non-Gaussian randomness. Meanwhile, the theoretical results of the SDC have been applied to several practical industrial applications. As data science raises based on the development of industrial artificial intelligence, SDC has been further developed recently focusing on data-driven design and multi-agent systems. To explore the new challenges with the evolution of SDC, e.g. unknown system models, unknown noise distributions, strong non-stationary transient dynamics, stability analysis and industrial applications, this survey summarises the most recent published results in the last 5 years of SDC work in terms of modelling, control, filtering, fault diagnosis, and industrial applications. Based on the technical analysis, potential future work is discussed in the end.

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Section: Fault Diagnosis For Non-gaussian Systemsmentioning

confidence: 99%

Recent Advances in Non-Gaussian Stochastic Systems Control Theory and Its Applications

Zhang

Zhou

2022

IJNDI

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“…Therefore, the controller design and performance analysis of stochastic systems are more difficult than that of general deterministic systems (Liu et al, 2016;Wang et al, 2014;Zhao et al, 2015). Driven by these problems, more and more attentions have been paid to the study of stochastic nonlinear systems Krstic, 1997, 1999;Deng et al, 2001;Feng and Shi, 2017;Ji and Xi, 2006;Li et al, 2017;Pan and Basar, 1999;Wang et al, 2013;Wei et al, 2019;Wu et al, 2010;Yao et al, 2019;Zhong et al, 2015). So far, many control methods of deterministic systems are successfully extended to stochastic systems, such as adaptive control (Ji and Xi, 2006;Wei et al, 2019;Wu et al, 2010), fault tolerant control (Yao et al, 2019), approximation-based control (Li et al, 2017;Wang et al, 2013;Zhong et al, 2015), sliding mode control (Feng and Shi, 2017) and backstepping technique Krstic, 1997, 1999;Deng et al, 2001;Pan and Basar, 1999).…”

Section: Introductionmentioning

confidence: 99%

“…Driven by these problems, more and more attentions have been paid to the study of stochastic nonlinear systems Krstic, 1997, 1999;Deng et al, 2001;Feng and Shi, 2017;Ji and Xi, 2006;Li et al, 2017;Pan and Basar, 1999;Wang et al, 2013;Wei et al, 2019;Wu et al, 2010;Yao et al, 2019;Zhong et al, 2015). So far, many control methods of deterministic systems are successfully extended to stochastic systems, such as adaptive control (Ji and Xi, 2006;Wei et al, 2019;Wu et al, 2010), fault tolerant control (Yao et al, 2019), approximation-based control (Li et al, 2017;Wang et al, 2013;Zhong et al, 2015), sliding mode control (Feng and Shi, 2017) and backstepping technique Krstic, 1997, 1999;Deng et al, 2001;Pan and Basar, 1999). In particular, due to the existence of unknown functions and uncertainty of systems, the approximation-based adaptive control methods, such as neural network (NN)-based control (Hua et al, 2015;Li et al, 2009;Zhou et al, 2012) and fuzzy-based control (Li and Yue, 2015;Ma et al, 2019;Zhou et al, 2017), have become a research hotspot in recent years.…”

Section: Introductionmentioning

confidence: 99%

Adaptive finite-time control for stochastic nonlinear systems using multi-dimensional Taylor network

Zhu

Wang

Han

2021

Transactions of the Institute of Measurement and Control

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In this paper, the problem of adaptive finite-time multi-dimensional Taylor network (MTN) control for a class of stochastic nonlinear systems is investigated. By combining the MTN-based approximate method and adaptive backstepping technique, a novel adaptive finite-time MTN control scheme is proposed. In this scheme, the MTNs are used to approximate the unknown nonlinear functions of the systems. The finite-time Lyapunov stability theory is utilized to prove the stability of the close-loop system. The proposed scheme can ensure that all signals in the closed-loop system are bounded in probability and the tracking error converges to a small neighborhood of the origin in a finite time. Three simulation examples are presented to show the effectiveness of the control scheme. It should be pointed that the adaptive MTN controller proposed in this paper has the advantages of fast computational speed and good real-time performance thanks to the simple structure of the MTN.

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“…3–6 Especially, the controller design of stochastic systems has become a research hotspot due to its important theoretical and practical values. 6 Through the efforts of researchers, many control methods of nonlinear systems have been naturally applied to nonlinear stochastic systems, such as fault tolerant control, 7 adaptive control, 8 backstepping technique, 9 and sliding mode control. 10 Especially, backstepping technique has become a common control method for nonlinear stochastic systems.…”

Section: Introductionmentioning

confidence: 99%

Adaptive multi-dimensional Taylor network control for nonlinear stochastic systems with time-delay

Zhu

Wang

Han

2021

Proceedings of the Institution of Mechanical Engineers, Part I:

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In this article, the problem of adaptive multi-dimensional Taylor network control for strict-feedback nonlinear stochastic systems with time-delay is investigated. To overcome the control degradation resulting from the delay terms, the appropriate integral-type Lyapunov–Krasovskii functions are introduced. A novel adaptive multi-dimensional Taylor network control scheme is provided via backstepping technique. The proposed adaptive multi-dimensional Taylor network controller can ensure that all signals in the closed-loop system are bounded in probability and the tracking error eventually converges to a small neighborhood of the origin. Three simulation examples are given to demonstrate the effectiveness of the proposed control scheme.

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Fault diagnosis and fault-tolerant control for non-Gaussian nonlinear stochastic systems via entropy optimisation

Cited by 6 publications

References 20 publications

Recent Advances in Non-Gaussian Stochastic Systems Control Theory and Its Applications

Recent Advances in Non-Gaussian Stochastic Systems Control Theory and Its Applications

Adaptive finite-time control for stochastic nonlinear systems using multi-dimensional Taylor network

Adaptive multi-dimensional Taylor network control for nonlinear stochastic systems with time-delay

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