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Robust Observer-Based Fault Diagnosis for Nonlinear Systems Using MATLAB®

Author : Jian Zhang

Publisher : Springer

Page : 231 pages

File Size : 14,76 MB

Release : 2016-05-27

Category : Technology & Engineering

ISBN : 3319323245

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Book Description

This book introduces several observer-based methods, including: • the sliding-mode observer • the adaptive observer • the unknown-input observer and • the descriptor observer method for the problem of fault detection, isolation and estimation, allowing readers to compare and contrast the different approaches. The authors present basic material on Lyapunov stability theory, H¥ control theory, sliding-mode control theory and linear matrix inequality problems in a self-contained and step-by-step manner. Detailed and rigorous mathematical proofs are provided for all the results developed in the text so that readers can quickly gain a good understanding of the material. MATLAB® and Simulink® codes for all the examples, which can be downloaded from http://extras.springer.com, enable students to follow the methods and illustrative examples easily. The systems used in the examples make the book highly relevant to real-world problems in industrial control engineering and include a seventh-order aircraft model, a single-link flexible joint robot arm and a satellite controller. To help readers quickly find the information they need and to improve readability, the individual chapters are written so as to be semi-independent of each other. Robust Oberserver-Based Fault Diagnosis for Nonlinear Systems Using MATLAB® is of interest to process, aerospace, robotics and control engineers, engineering students and researchers with a control engineering background.



Robust Observer Based Fault Diagnosis for Nonlinear Systems

Author : Jian Zhang

Publisher :

Page : 176 pages

File Size : 23,99 MB

Release : 2013

Category : Electric fault location

ISBN :

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Book Description

The field of observer based fault diagnosis for nonlinear systems has become an important topic of research in the control community over the last three decades. In this thesis, the issues of robust fault detection, isolation and estimation of actuator faults and sensor faults for Lipschitz nonlinear systems has been studied using sliding mode, adaptive and descriptor system approaches. The problem of estimating actuator faults is initially discussed. The sliding mode observer (SMO) is constructed directly based on the uncertain nonlinear system. The fault is reconstructed using the concept of equivalent output injection. Sensor faults are treated as actuator faults by using integral observer based approach and then the problem of sensor fault diagnosis, including detection, isolation and estimation is studied. The proposed scheme has the ability of successfully diagnosing incipient sensor faults in the presence of system uncertainties. The results are then extended to simultaneously estimate actuator faults and sensor faults using SMOs, adaptive observers (AO) and descriptor system approaches. H_ filtering is integrated into the observers to ensure that the fault estimation error as well as the state estimation error are less than a prescribed performance level. The existence of the proposed fault estimators and their stability analysis are carried out in terms of LMIs. It has been observed that when the Lipschitz constant is unknown or too large, it may fail to find feasible solutions for observers. In order to deal with this situation, adaptation laws are used to generate an additional control input to the nonlinear system. The additional control input can eliminate the effect of Lipschitz constant on the solvability of LMIs. The effectiveness of various methods proposed in this research has been demonstrated using several numerical and practical examples. The simulation results demonstrate that the proposed methods can achieve the prescribed performance requirements.





Advances in Data Science and Computing Technologies

Author : Basabi Chakraborty

Publisher : Springer Nature

Page : 738 pages

File Size : 32,24 MB

Release : 2023-09-29

Category : Technology & Engineering

ISBN : 981993656X

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Book Description

This book presents selected research papers on current developments in artificial intelligence (AI) and data sciences from the International Conference on Advances in Data Science and Computing Technologies, ADSC 2022. The book covers topics such as soft computing techniques, AI, optical communication systems, application of Internet of Things, hybrid and renewable energy sources, cloud and mobile computing, deep machine learning, data networks & securities. The book discusses various aspects of these topics, e.g., technological considerations, product implementation, and application issues. The volume will serve as a reference resource for researchers and practitioners in academia and industry.



Smart Embedded Systems

Author : Arun Sinha

Publisher : CRC Press

Page : 297 pages

File Size : 39,53 MB

Release : 2023-12-01

Category : Technology & Engineering

ISBN : 1003810357

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Book Description

"Smart Embedded Systems: Advances and Applications" is a comprehensive guide that demystifies the complex world of embedded technology. The book journeys through a wide range of topics from healthcare to energy management, autonomous robotics, and wireless communication, showcasing the transformative potential of intelligent embedded systems in these fields. This concise volume introduces readers to innovative techniques and their practical applications, offers a comparative analysis of wireless protocols, and provides efficient resource allocation strategies in IoT-based ecosystems. With real-world examples and in-depth case studies, it serves as an invaluable resource for students and professionals seeking to harness the power of embedded technology to shape our digital future. Salient Features: 1. The book provides a comprehensive coverage of various aspects of smart embedded systems, exploring their design, implementation, optimization, and a range of applications. This is further enhanced by in-depth discussions on hardware and software optimizations aimed at improving overall system performance. 2. A detailed examination of machine learning techniques specifically tailored for data analysis and prediction within embedded systems. This complements the exploration of cutting-edge research on the use of AI to enhance wireless communications. 3. Real-world applications of these technologies are extensively discussed, with a focus on areas such as seizure detection, noise reduction, health monitoring, diabetic care, autonomous vehicles, and communication systems. This includes a deep-dive into different wireless protocols utilized for data transfer in IoT systems. 4. This book highlights key IoT technologies and their myriad applications, extending from environmental data collection to health monitoring. This is underscored by case studies on the integration of AI and IoT in healthcare, spanning topics from anomaly detection to informed clinical decision-making. Also featured is a detailed evaluation and comparison of different system implementations and methodologies. This book is an essential read for anyone interested in the field of embedded systems. Whether you're a student looking to broaden your knowledge base, researchers looking in-depth insights, or professionals planning to use this cutting-edge technology in real-world applications, this book offers a thorough grounding in the subject.





Robust Integration of Model-Based Fault Estimation and Fault-Tolerant Control

Author : Jianglin Lan

Publisher : Springer Nature

Page : 275 pages

File Size : 46,7 MB

Release : 2020-12-11

Category : Technology & Engineering

ISBN : 3030587606

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Book Description

Robust Integration of Model-Based Fault Estimation and Fault-Tolerant Control is a systematic examination of methods used to overcome the inevitable system uncertainties arising when a fault estimation (FE) function and a fault-tolerant controller interact as they are employed together to compensate for system faults and maintain robustly acceptable system performance. It covers the important subject of robust integration of FE and FTC with the aim of guaranteeing closed-loop stability. The reader’s understanding of the theory is supported by the extensive use of tutorial examples, including some MATLAB®-based material available from the Springer website and by industrial-applications-based material. The text is structured into three parts: Part I examines the basic concepts of FE and FTC, providing extensive insight into the importance of and challenges involved in their integration; Part II describes five effective strategies for the integration of FE and FTC: sequential, iterative, simultaneous, adaptive-decoupling, and robust decoupling; and Part III begins to extend the proposed strategies to nonlinear and large-scale systems and covers their application in the fields of renewable energy, robotics and networked systems. The strategies presented are applicable to a broad range of control problems, because in the absence of faults the FE-based FTC naturally reverts to conventional observer-based control. The book is a useful resource for researchers and engineers working in the area of fault-tolerant control systems, and supplementary material for a graduate- or postgraduate-level course on fault diagnosis and FTC. Advances in Industrial Control reports and encourages the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.



Model Based Fault Diagnosis in Complex Control Systems

Author : Weitian Chen

Publisher :

Page : 460 pages

File Size : 36,55 MB

Release : 2007

Category : Failure analysis (Engineering)

ISBN :

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Book Description

This thesis deals with model based fault diagnosis problems for several classes of systems with complexities such as uncertainties and nonlinearities. To deal with system complexities, robust and adaptive approaches are used as the main tools. To focus more on fault isolation and estimation, novel observer and output estimator based fault diagnosis schemes are proposed. Chapters 2 to 4 employ robust approaches to deal with complexities such as nonlinearities and nonparametric uncertainties. Robust observers, that is, Unknown Input Observers (UIOs) and Sliding Mode Observers (SMOs), are designed to solve fault diagnosis problems for Lipschitz nonlinear systems and Takagi-Sugeno fuzzy system represented uncertain nonlinear systems. UIO and SMO based fault diagnosis schemes, whose main novelty lies in the fault isolation, are proposed. Chapters 5 and 6 also use robust approaches to attack more challenging complexities such as unmatched uncertainties. A novel idea which advocates output estimator design and abandons the state observer design is proposed. Robust output estimator based fault diagnosis schemes are developed for a class of linear systems with both matched and unmatched non-parametric uncertainties. The output estimator approach is extended to a more general class of linear systems, and a high-order sliding mode differentiator based actuator fault diagnosis scheme is designed, which is the first in fault diagnosis. Chapters 7 and 8 use adaptive approaches to cope with complexities such as parametric uncertainties. Adaptive output estimator based fault diagnosis schemes are designed for sensor and actuator fault diagnosis problems in unknown linear Multi-Input Multi-Output (MIMO) and Multi-Input Single-Output (MISO) systems. A novel idea involving integration of fault isolation design functions into controller designs is put forward in actuator fault diagnosis. The results in this thesis demonstrate that: 1) the proposed robust observer based fault diagnosis schemes are powerful in dealing with matched uncertainties and certain types of nonlinearities; 2) the proposed robust output estimator (and output derivative estimator) based fault diagnosis schemes are powerful in counteracting unmatched non-parametric uncertainties; and 3) the adaptive output estimator approach is very promising and powerful in coping with parametric uncertainties. The thesis concludes by discussing important open problems for future research.



Fault Detection and Diagnosis in Nonlinear Systems

Author : Rafael Martinez-Guerra

Publisher : Springer

Page : 143 pages

File Size : 23,66 MB

Release : 2013-11-19

Category : Technology & Engineering

ISBN : 3319030477

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Book Description

The high reliability required in industrial processes has created the necessity of detecting abnormal conditions, called faults, while processes are operating. The term fault generically refers to any type of process degradation, or degradation in equipment performance because of changes in the process's physical characteristics, process inputs or environmental conditions. This book is about the fundamentals of fault detection and diagnosis in a variety of nonlinear systems which are represented by ordinary differential equations. The fault detection problem is approached from a differential algebraic viewpoint, using residual generators based upon high-gain nonlinear auxiliary systems (‘observers’). A prominent role is played by the type of mathematical tools that will be used, requiring knowledge of differential algebra and differential equations. Specific theorems tailored to the needs of the problem-solving procedures are developed and proved. Applications to real-world problems, both with constant and time-varying faults, are made throughout the book and include electromechanical positioning systems, the Continuous Stirred Tank Reactor (CSTR), bioreactor models and belt drive systems, to name but a few.