Radial Basis Function (RBF) Neural Network Control for Mechanical Systems is motivated by the need for systematic design approaches to stable adaptive control system design using neural network approximation-based techniques. The main objectives of the book are to introduce the concrete design methods and MATLAB simulation of stable adaptive RBF neural control strategies. In this book, a broad range of implementable neural network control design methods for mechanical systems are presented, such as robot manipulators, inverted pendulums, single link flexible joint robots, motors, etc. Advanced neural network controller design methods and their stability analysis are explored. The book provides readers with the fundamentals of neural network control system design. This book is intended for the researchers in the fields of neural adaptive control, mechanical systems, Matlab simulation, engineering design, robotics and automation. Jinkun Liu is a professor at Beijing University of Aeronautics and Astronautics.

"Advanced Sliding Mode Control for Mechanical Systems: Design, Analysis and MATLAB Simulation" takes readers through the basic concepts, covering the most recent research in sliding mode control. The book is written from the perspective of practical engineering and examines numerous classical sliding mode controllers, including continuous time sliding mode control, discrete time sliding mode control, fuzzy sliding mode control, neural sliding mode control, backstepping sliding mode control, dynamic sliding mode control, sliding mode control based on observer, terminal sliding mode control, sliding mode control for robot manipulators, and sliding mode control for aircraft. This book is intended for engineers and researchers working in the field of control. Dr. Jinkun Liu works at Beijing University of Aeronautics and Astronautics and Dr. Xinhua Wang works at the National University of Singapore.

From the ox carts and pottery wheels the spacecrafts and disk drives, efficiency and quality has always been dependent on the engineer’s ability to anticipate and control the effects of vibration. And while progress in negating the noise, wear, and inefficiency caused by vibration has been made, more is needed. Modeling and Control of Vibration in Mechanical Systems answers the essential needs of practitioners in systems and control with the most comprehensive resource available on the subject. Written as a reference for those working in high precision systems, this uniquely accessible volume: Differentiates between kinds of vibration and their various characteristics and effects Offers a close-up look at mechanical actuation systems that are achieving remarkably high precision positioning performance Includes techniques for rejecting vibrations of different frequency ranges Covers the theoretical developments and principles of control design with detail elaborate enough that readers will be able to apply the techniques with the help of MATLAB® Details a wealth of practical working examples as well as a number of simulation and experimental results with comprehensive evaluations The modern world’s ever-growing spectra of sophisticated engineering systems such as hard disk drives, aeronautic systems, and manufacturing systems have little tolerance for unanticipated vibration of even the slightest magnitude. Accordingly, vibration control continues to draw intensive focus from top control engineers and modelers. This resource demonstrates the remarkable results of that focus to date, and most importantly gives today’s researchers the technology that they need to build upon into the future. Chunling Du is currently researching modeling and advanced servo control of hard disk drives at the Data Storage Institute in Singapore. Lihua Xie is the Director of the Centre for Intelligent Machines and a professor at Nanyang Technological University in Singapore.

This proceedings book gathers papers presented at the 4th International Conference on Advanced Engineering Theory and Applications 2017 (AETA 2017), held on 7–9 December 2017 at Ton Duc Thang University, Ho Chi Minh City, Vietnam. It presents selected papers on 13 topical areas, including robotics, control systems, telecommunications, computer science and more. All selected papers represent interesting ideas and collectively provide a state-of-the-art overview. Readers will find intriguing papers on the design and implementation of control algorithms for aerial and underwater robots, for mechanical systems, efficient protocols for vehicular ad hoc networks, motor control, image and signal processing, energy saving, optimization methods in various fields of electrical engineering, and others. The book also offers a valuable resource for practitioners who want to apply the content discussed to solve real-life problems in their challenging applications. It also addresses common and related subjects in modern electric, electronic and related technologies. As such, it will benefit all scientists and engineers working in the above-mentioned fields of application.

This book focuses on the integration of intelligent communication systems, control systems and devices related to all aspects of engineering and sciences. It includes high-quality research papers from the 4th International Conference on Intelligent Communication, Control and Devices (ICICCD 2020), organized by the Department of Electronics, Instrumentation and Control Engineering at the University of Petroleum and Energy Studies, Dehradun, India during 27–28 November 2020. The topics covered are a range of recent advances in intelligent communication, intelligent control, and intelligent devices.

Discrete-Time Neural Observers: Analysis and Applications presents recent advances in the theory of neural state estimation for discrete-time unknown nonlinear systems with multiple inputs and outputs. The book includes rigorous mathematical analyses, based on the Lyapunov approach, that guarantee their properties. In addition, for each chapter, simulation results are included to verify the successful performance of the corresponding proposed schemes. In order to complete the treatment of these schemes, the authors also present simulation and experimental results related to their application in meaningful areas, such as electric three phase induction motors and anaerobic process, which show the applicability of such designs. The proposed schemes can be employed for different applications beyond those presented. The book presents solutions for the state estimation problem of unknown nonlinear systems based on two schemes. For the first one, a full state estimation problem is considered; the second one considers the reduced order case with, and without, the presence of unknown delays. Both schemes are developed in discrete-time using recurrent high order neural networks in order to design the neural observers, and the online training of the respective neural networks is performed by Kalman Filtering. Presents online learning for Recurrent High Order Neural Networks (RHONN) using the Extended Kalman Filter (EKF) algorithm Contains full and reduced order neural observers for discrete-time unknown nonlinear systems, with and without delays Includes rigorous analyses of the proposed schemes, including the nonlinear system, the respective observer, and the Kalman filter learning Covers real-time implementation and simulation results for all the proposed schemes to meaningful applications