IEEE Press on Control Systems Theory and Applications

Comprehensive reference delivering basic principles and state-of-the-art parameter estimation techniques for permanent magnet synchronous machines (PMSMs) Parameter Estimation of Permanent Magnet Synchronous Machines reviews estimation techniques of the parameters of PMSMs, introducing basic models and techniques, as well as issues and solutions in parameter estimation challenges, including rank deficiency, inverter nonlinearity, and magnetic saturation. This book is supported by theories, experiments, and simulation examples for each technique covered. Topics explored in this book include: • Electrical and mechanical parameter estimation techniques, including those based on current/voltage injection and position offset injection, under constant or variable speed and load for sensored or sensorless controlled PMSMs, accounting for magnetic saturation, cross-coupling, inverter nonlinearity, temperature effects, and more • Recursive least squares, the Kalman filter, model reference adaptive systems, Adaline neural networks, gradient-based methods, particle swarm optimization, and genetic algorithms • Applications of parameter estimation techniques for improvement of control performance, sensorless control, thermal condition monitoring, and fault diagnosis This book is an essential reference for professionals working on the control and design of electrical machines, researchers studying electric vehicles, wind power generators, aerospace, industrial drives, automation systems, robots, and domestic appliances, as well as advanced undergraduate and graduate students in related programs of study.

Comprehensive, fast-access guide to different types of sliding mode controllers and their programming and simulation in MATLAB and Simulink Methods of Developing Sliding Mode Controllers delivers a practical review of sliding mode controllers (SMCs) and their challenges with coverage of related theorems, stability analysis, and how to program and simulate SMCs in MATLAB and Simulink. The book details the latest methods of their development and their applications in the automotive, aerospace, and robotics industries. Initial chapters detail a range of different types of controllers. A combination of sliding and backstepping control is introduced and simulated and the phenomenon of chattering and effective solutions to reduce it are provided, along with suitable examples and analytical tables of the results. The final two chapters are related to fixed-time and event-triggered SMCs. Extensive Matlab/Simulink supported examples and simulation program code/block diagrams are included throughout. Methods of Developing Sliding Mode Controllers: Design and Matlab Simulation explores sample topics including: • Classic SMCs, covering variable structures, including relays and feedback control with switching gains, as well as controller design and theoretical foundations • Terminal SMCs, covering nonsingular and fast variations, dynamic SMCs, and fuzzy SMCs, covering fuzzy approximation and equivalent control as well as indirect design • Super twisting SMCs, adaptive SMCs, and backstepping SMCs, covering the backstepping method and chaotic duffing oscillator equations • Sign, Epsilon-sign, saturation, hyperbolic tangent, and generalized hyperbolic tangent functions for chatter reduction Methods of Developing Sliding Mode Controllers: Design and Matlab Simulation is a concise yet comprehensive and highly practical reference on the subject for graduate/postgraduate students in electrical engineering, mechanical engineering, and biomedical engineering along with academics and professionals in fields related to SMCs.

Comprehensive treatment of several representative flexible systems, ranging from dynamic modeling and intelligent control design through to stability analysis Fully illustrated throughout, Dynamic Modeling and Neural Network-Based Intelligent Control of Flexible Systems proposes high-efficiency modeling methods and novel intelligent control strategies for several representative flexible systems developed by means of neural networks. It discusses tracking control of multi-link flexible manipulators, vibration control of flexible buildings under natural disasters, and fault-tolerant control of bionic flexible flapping-wing aircraft and addresses common challenges like external disturbances, dynamic uncertainties, output constraints, and actuator faults. Expanding on its theoretical deliberations, the book includes many case studies demonstrating how the proposed approaches work in practice. Experimental investigations are carried out on Quanser Rotary Flexible Link, Quanser 2 DOF Serial Flexible Link, Quanser Active Mass Damper, and Quanser Smart Structure platforms. The book starts by providing an overview of dynamic modeling and intelligent control of flexible systems, introducing several important issues, along with modeling and control methods of three typical flexible systems. Other topics include: • Foundational mathematical preliminaries including the Hamilton principle, model discretization methods, Lagrange's equation method, and Lyapunov's stability theorem • Dynamic modeling of a single-link flexible robotic manipulator and vibration control design for a string with the boundary time-varying output constraint • Unknown time-varying disturbances, such as earthquakes and strong winds, and how to suppress them and use MATLAB and Quanser to verify effectiveness of a proposed control • Adaptive vibration control methods for a single-floor building-like structure equipped with an active mass damper (AMD) Dynamic Modeling and Neural Network-Based Intelligent Control of Flexible Systems is an invaluable resource for researchers and engineers seeking high-efficiency modeling methods and neural-network-based control solutions for flexible systems, along with industry engineers and researchers who are interested in control theory and applications and students in related programs of study.