Recent Developments In Model Based And Data Driven Methods For Advanced Control And Diagnosis

The book consists of recent works on several axes either with a more theoretical nature or with a focus on applications, which will span a variety of up-to-date topics in the field of systems and control. The main market area of the contributions include: Advanced fault-tolerant control, control reconfiguration, health monitoring techniques for industrial systems, data-driven diagnosis methods, process supervision, diagnosis and control of discrete-event systems, maintenance and repair strategies, statistical methods for fault diagnosis, reliability and safety of industrial systems artificial intelligence methods for control and diagnosis, health-aware control design strategies, advanced control approaches, deep learning-based methods for control and diagnosis, reinforcement learning-based approaches for advanced control, diagnosis and prognosis techniques applied to industrial problems, Industry 4.0 as well as instrumentation and sensors. These works constitute advances in the aforementioned scientific fields and will be used by graduate as well as doctoral students along with established researchers to update themselves with the state of the art and recent advances in their respective fields. As the book includes several applicative studies with several multi-disciplinary contributions (deep learning, reinforcement learning, model-based/data-based control etc.), the book proves to be equally useful for the practitioners as well industrial professionals.

Modern Spacecraft Guidance, Navigation, and Control: From System Modeling to AI and Innovative Applications provides a comprehensive foundation of theory and applications of spacecraft GNC, from fundamentals to advanced concepts, including modern AI-based architectures with focus on hardware and software practical applications. Divided into four parts, this book begins with an introduction to spacecraft GNC, before discussing the basic tools for GNC applications. These include an overview of the main reference systems and planetary models, a description of the space environment, an introduction to orbital and attitude dynamics, and a survey on spacecraft sensors and actuators, with details of their modeling principles. Part 2 covers guidance, navigation, and control, including both on-board and ground-based methods. It also discusses classical and novel control techniques, failure detection isolation and recovery (FDIR) methodologies, GNC verification, validation, and on-board implementation. The final part 3 discusses AI and modern applications featuring different applicative scenarios, with particular attention on artificial intelligence and the possible benefits when applied to spacecraft GNC. In this part, GNC for small satellites and CubeSats is also discussed. Modern Spacecraft Guidance, Navigation, and Control: From System Modeling to AI and Innovative Applications is a valuable resource for aerospace engineers, GNC/AOCS engineers, avionic developers, and AIV/AIT technicians. Provides an overview of classical and modern GNC techniques, covering practical system modeling aspects and applicative cases Presents the most important artificial intelligence algorithms applied to present and future spacecraft GNC Describes classical and advanced techniques for GNC hardware and software verification and validation and GNC failure detection isolation and recovery (FDIR)

Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems presents basic statistical process monitoring, fault diagnosis, and control methods and introduces advanced data-driven schemes for the design of fault diagnosis and fault-tolerant control systems catering to the needs of dynamic industrial processes. With ever increasing demands for reliability, availability and safety in technical processes and assets, process monitoring and fault-tolerance have become important issues surrounding the design of automatic control systems. This text shows the reader how, thanks to the rapid development of information technology, key techniques of data-driven and statistical process monitoring and control can now become widely used in industrial practice to address these issues. To allow for self-contained study and facilitate implementation in real applications, important mathematical and control theoretical knowledge and tools are included in this book. Major schemes are presented in algorithm form and demonstrated on industrial case systems. Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems will be of interest to process and control engineers, engineering students and researchers with a control engineering background.

This book presents systematic overviews and bright insights into big data-driven intelligent fault diagnosis and prognosis for mechanical systems. The recent research results on deep transfer learning-based fault diagnosis, data-model fusion remaining useful life (RUL) prediction, etc., are focused on in the book. The contents are valuable and interesting to attract academic researchers, practitioners, and students in the field of prognostics and health management (PHM). Essential guidelines are provided for readers to understand, explore, and implement the presented methodologies, which promote further development of PHM in the big data era. Features: Addresses the critical challenges in the field of PHM at present Presents both fundamental and cutting-edge research theories on intelligent fault diagnosis and prognosis Provides abundant experimental validations and engineering cases of the presented methodologies

This contributed volume is written by key specialists working in multidisciplinary fields in electrical engineering, linking control theory, power electronics, artificial neural networks, embedded controllers and signal processing. The authors of each chapter report the state of the art of the various topics addressed and present results of their own research, laboratory experiments and successful applications. The presented solutions concentrate on three main areas of interest: · motion control in complex electromechanical systems, including sensorless control; · fault diagnosis and fault tolerant control of electric drives; · new control algorithms for power electronics converters. The chapters and the complete book possess strong monograph attributes. Important practical and theoretical problems are deeply and accurately presented on the background of an exhaustive state-of the art review. Many results are completely new and were never published before. Well-known control methods like field oriented control (FOC) or direct torque control (DTC) are referred as a starting point for modifications or are used for comparison. Among numerous control theories used to solve particular problems are: nonlinear control, robust control, adaptive control, Lyapunov techniques, observer design, model predictive control, neural control, sliding mode control, signal filtration and processing, fault diagnosis, and fault tolerant control.

This book presents technologies and solutions related to the test and launch control of rockets and other vehicles, and offers the first comprehensive and systematic introduction to the contributions of the Chinese Long March (Chang Zheng in Chinese, or abbreviated as CZ) rockets in this field. Moreover, it discusses the role of this technology in responsive, reliable, and economical access to space, which is essential for the competitiveness of rockets. The need for rapid development of the aerospace industry for both governmental and commercial projects is addressed. This book is a valuable reference resource for practitioners, and many examples and resources are included, not only from Chinese rockets but also from many other vehicles. It covers guidelines, technologies, and solutions on testing and launch control before rocket takeoff, covering equipment-level testing, system-level testing, simulation tests, etc.

Fault Diagnosis and Sustainable Control of Wind Turbines: Robust Data-Driven and Model-Based Strategies discusses the development of reliable and robust fault diagnosis and fault-tolerant (‘sustainable’) control schemes by means of data-driven and model-based approaches. These strategies are able to cope with unknown nonlinear systems and noisy measurements. The book also discusses simpler solutions relying on data-driven and model-based methodologies, which are key when on-line implementations are considered for the proposed schemes. The book targets both professional engineers working in industry and researchers in academic and scientific institutions. In order to improve the safety, reliability and efficiency of wind turbine systems, thus avoiding expensive unplanned maintenance, the accommodation of faults in their early occurrence is fundamental. To highlight the potential of the proposed methods in real applications, hardware–in–the–loop test facilities (representing realistic wind turbine systems) are considered to analyze the digital implementation of the designed solutions. The achieved results show that the developed schemes are able to maintain the desired performances, thus validating their reliability and viability in real-time implementations. Different groups of readers—ranging from industrial engineers wishing to gain insight into the applications' potential of new fault diagnosis and sustainable control methods, to the academic control community looking for new problems to tackle—will find much to learn from this work. Provides wind turbine models with varying complexity, as well as the solutions proposed and developed by the authors Addresses in detail the design, development and realistic implementation of fault diagnosis and fault tolerant control strategies for wind turbine systems Addresses the development of sustainable control solutions that, in general, do not require the introduction of further or redundant measurements Proposes active fault tolerant ('sustainable') solutions that are able to maintain the wind turbine working conditions with gracefully degraded performance before required maintenance can occur Presents full coverage of the diagnosis and fault tolerant control problem, starting from the modeling and identification and finishing with diagnosis and fault tolerant control approaches Provides MATLAB and Simulink codes for the solutions proposed

Early and accurate fault detection and diagnosis for modern chemical plants can minimize downtime, increase the safety of plant operations, and reduce manufacturing costs. This book presents the theoretical background and practical techniques for data-driven process monitoring. It demonstrates the application of all the data-driven process monitoring techniques to the Tennessee Eastman plant simulator, and looks at the strengths and weaknesses of each approach in detail. A plant simulator and problems allow readers to apply process monitoring techniques.