Описание: The book covers different subjects in the field of nonlinear dynamics, especially applications and investigation of chaos and chaotic systems in electrical engineering, information technology, communication engineering and mechanical engineering. This book is suitable as a textbook at the graduate or advanced undergraduate level and will appeal to postgraduate-level students and young researchers in different fields. This book provides technological advancement in nonlinear dynamics and chaos and explores the fields of communication, electric vehicles, power systems and rotational machines with centrifugal flyball governor system. An autonomous chaotic system is explored with real and complex state variables; their projective synchronisation is reported with application to secure communication. Secure communication is achieved using Masking-Modulation and Diffie-Hellman Key Exchange encryption techniques. Further, electric vehicles are the necessity of upcoming trends. To optimize the control performance of the permanent-magnet synchronous motor with different disturbances and uncertainties, a nonlinear control for the permanent-magnet synchronous motor using sliding-mode control is reported and Cascaded PI sliding mode control technique is explored to control the chaotic behaviour in electric vehicles. Chaos behaviour is explored in power systems and its control is presented using higher order sliding mode control. Comparative performances are analysed followed by control of chaos in the Rotational Machine with Centrifugal Flyball Governor system where chaos is controlled using recursive backstepping sliding mode control. All the simulations are carried out in the MATLAB environment and reveal successful achievement of the objectives. Researchers from academia and industry, who are working in the research areas Nonlinear Dynamical Systems & Chaos, Electrical Engineering, Computer Science Engineering, Information Technology, Communication Engineering and Mechanical Engineering may be principal audiences. Also, the book will be helpful for (i) graduate or advanced undergraduate level students as a textbook or major reference book for courses such as electrical circuits, nonlinear dynamical systems, mathematical modelling, computational science, numerical simulation, and many others and (ii) postgraduate level students and young researchers in the following fields: Communication Engineering; Computer Science; Electrical and Electronic Engineering; Mechanical Engineering; Engineering Mathematics; Computational Physics.
Описание: The focus of this book is the application of artificial neural networks in uncertain dynamical systems. It explains how to use neural networks in concert with adaptive techniques for system identification, state estimation, and control problems. The authors begin with a brief historical overview of adaptive control, followed by a review of mathematical preliminaries. In the subsequent chapters, they present several neural network-based control schemes. Each chapter starts with a concise introduction to the problem under study, and a neural network-based control strategy is designed for the simplest case scenario. After these designs are discussed, different practical limitations (i.e., saturation constraints and unavailability of all system states) are gradually added, and other control schemes are developed based on the primary scenario. Through these exercises, the authors present structures that not only provide mathematical tools for navigating control problems, but also supply solutions that are pertinent to real-life systems.
Автор: Uday Pratap Singh, Akhilesh Tiwari, Rajeev Kumar Singh Название: Soft-Computing-Based Nonlinear Control Systems Design ISBN: 1522535314 ISBN-13(EAN): 9781522535317 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 35759.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: A critical part of ensuring that systems are advancing alongside technology without complications is problem solving. Practical applications of problem-solving theories can model conflict and cooperation and aid in creating solutions to real-world problems. Soft-Computing-Based Nonlinear Control Systems Design is a critical scholarly publication that examines the practical applications of control theory and its applications in problem solving to fields including economics, environmental management, and financial modelling. Featuring a wide range of topics, such as fuzzy logic, nature-inspired algorithms, and cloud computing, this book is geared toward academicians, researchers, and students seeking relevant research on control theory and its practical applications.
Автор: Khorrami, F. Krishnamurthy, Prashant Melkote, H. Название: Modeling and adaptive nonlinear control of electric motors ISBN: 3642056679 ISBN-13(EAN): 9783642056673 Издательство: Springer Рейтинг: Цена: 32651.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This research monograph considers modelling and control design of electric motors, namely step motors, brushless DC motors and induction motors. The book reports new global robust adaptive designs for motors and provides new tools for control designs, with an emphasis on stepper motors.
Автор: Martin Guay, Veronica Adetola and Darryl De Haan Название: Robust And Adaptive Model Predictive Control Of Nonlinear Systems ISBN: 1849195528 ISBN-13(EAN): 9781849195522 Издательство: Неизвестно Рейтинг: Цена: 24944.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book offers a novel approach to adaptive control and provides a sound theoretical background to designing robust adaptive control systems with guaranteed transient performance. It focuses on the more typical role of adaptation as a means of coping with uncertainties in the system model.
Автор: Esfandiari Kasra, Abdollahi Farzaneh, Talebi Heidar A. Название: Neural Network-Based Adaptive Control of Uncertain Nonlinear Systems ISBN: 3030731359 ISBN-13(EAN): 9783030731359 Издательство: Springer Рейтинг: Цена: 22359.00 р. Наличие на складе: Поставка под заказ.
Описание: The focus of this book is the application of artificial neural networks in uncertain dynamical systems. It explains how to use neural networks in concert with adaptive techniques for system identification, state estimation, and control problems. The authors begin with a brief historical overview of adaptive control, followed by a review of mathematical preliminaries. In the subsequent chapters, they present several neural network-based control schemes. Each chapter starts with a concise introduction to the problem under study, and a neural network-based control strategy is designed for the simplest case scenario. After these designs are discussed, different practical limitations (i.e., saturation constraints and unavailability of all system states) are gradually added, and other control schemes are developed based on the primary scenario. Through these exercises, the authors present structures that not only provide mathematical tools for navigating control problems, but also supply solutions that are pertinent to real-life systems.
This book reports on the latest advances in adaptive critic control with robust stabilization for uncertain nonlinear systems. Covering the core theory, novel methods, and a number of typical industrial applications related to the robust adaptive critic control field, it develops a comprehensive framework of robust adaptive strategies, including theoretical analysis, algorithm design, simulation verification, and experimental results. As such, it is of interest to university researchers, graduate students, and engineers in the fields of automation, computer science, and electrical engineering wishing to learn about the fundamental principles, methods, algorithms, and applications in the field of robust adaptive critic control. In addition, it promotes the development of robust adaptive critic control approaches, and the construction of higher-level intelligent systems.
Adaptive Sliding Mode Neural Network Control for Nonlinear Systems introduces nonlinear systems basic knowledge, analysis and control methods, and applications in various fields. It offers instructive examples and simulations, along with the source codes, and provides the basic architecture of control science and engineering.
Introduces nonlinear systems' basic knowledge, analysis and control methods, along with applications in various fields
Offers instructive examples and simulations, including source codes
Provides the basic architecture of control science and engineering
Автор: A.L. Fradkov; I.V. Miroshnik; V.O. Nikiforov Название: Nonlinear and Adaptive Control of Complex Systems ISBN: 9048152941 ISBN-13(EAN): 9789048152940 Издательство: Springer Рейтинг: Цена: 15372.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: T his book presents a t.hooretical framewerk and control methodology for a class of complcx dyna.mical systenis characterized by high state space dimension, multiple inpu t.s anrl out puts. significant nonlinearity, parametric uncertainty and unmodellod dyuarni cs. The book start.s wit.h an inl.rod uct.orv Chapter 1 where the peculiari- ties of control problcrns Ior complex systems are discussed and motivating examples from different fiolds of seience and technology are given. Chapter 2 prcscnts SO Il I(' rcsults of nonlinear control theory which assist in reading subsequent chaptors. The main notions and concepts of stability theory are int roduced. and problems of nonlinear transformation of sys- tem coordinates an' discussod. On this basis, we consider different design techniques and approaches t 0 linearization. stabilization and passification of nonlinear dynamical SySt('IIIS. Chapter 3 gives an cx posit.ion of the Speed-Gradient method and its ap- plications to nonlinear aud adaptive control. Convergence and robustness properties are exam iued. I roblcms of rcgulat ion, tracking, partial stabiliza- tion and control of 11amiItonia.n systerns are considered .
Описание: This book provides a basic understanding of adaptive control and its applications in Flight control. It also discusses control methodologies and the application of control techniques which will help practicing flight control and active flow control researchers.
This book reports on the latest advances in adaptive critic control with robust stabilization for uncertain nonlinear systems. Covering the core theory, novel methods, and a number of typical industrial applications related to the robust adaptive critic control field, it develops a comprehensive framework of robust adaptive strategies, including theoretical analysis, algorithm design, simulation verification, and experimental results. As such, it is of interest to university researchers, graduate students, and engineers in the fields of automation, computer science, and electrical engineering wishing to learn about the fundamental principles, methods, algorithms, and applications in the field of robust adaptive critic control. In addition, it promotes the development of robust adaptive critic control approaches, and the construction of higher-level intelligent systems.
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