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Modeling and Nonlinear Robust Control of Delta-Like Parallel Kinematic Manipulators deals with the modeling and control of parallel robots. The book's content will benefit students, researchers and engineers in robotics by providing a simplified methodology to obtain the dynamic model of parallel robots with a delta-type architecture. Moreover, this methodology is compatible with the real-time implementation of model-based and robust control schemes. And, it can easily extend the proposed robust control solutions to other robotic architectures. Introduces a novel parallel robot designed for machining operations called SPIDER4 Presents a mathematical formulation of the kinematic and dynamic models of SPIDER4 Offers validation of the computed mathematical models and designed controllers through real-time experiments under different operating conditions

The purpose of the book is to provide an exposition of recently developed adaptive and fault-tolerant control of underactuated nonlinear systems. Underactuated systems are abundant in real life, ranging from landing vehicles to surface ships and underwater vehicles to spacecrafts. For the tracking and stabilization control of underactuated mechanical systems, many methodologies have been proposed. However, a number of important issues deserve further investigation. In response to these issues, four important problems are solved in this book, including control of underactuated nonlinear systems with input saturation, output-feedback control in the presence of parametric uncertainties, fault-tolerant control of underactuated ships with or without actuator redundancy, and adaptive control of multiple underactauted nonlinear systems, including formation control and flocking control of multiple underactuated systems.

The book presents recent advances in the theory of neural control for discrete-time nonlinear systems with multiple inputs and multiple outputs. The simulation results that appear in each chapter include rigorous mathematical analyses, based on the Lyapunov approach, to establish its properties. The book contains two sections: the first focuses on the analyses of control techniques; the second is dedicated to illustrating results of real-time applications. It also provides solutions for the output trajectory tracking problem of unknown nonlinear systems based on sliding modes and inverse optimal control scheme.'This book on Discrete-time Recurrent Neural Control is unique in the literature, with new knowledge and information about the new technique of recurrent neural control especially for discrete-time systems.The book is well organized and clearly presented. It will be welcome by a wide range of researchers in science and engineering, especially graduate students and junior researchers who want to learn the new notion of recurrent neural control. I believe it will have a good market.It is an excellent book after all.'— Guanrong Chen, City University of Hong Kong'This book includes very relevant topics, about neural control. In these days, Artificial Neural Networks have been recovering their relevance and well-stablished importance, this due to its great capacity to process big amounts of data. Artificial Neural Networks development always is related to technological advancements; therefore, it is not a surprise that now we are being witnesses of this new era in Artificial Neural Networks, however most of the developments in this research area only focuses on applicability of the proposed schemes. However, Edgar N. Sanchez author of this book does not lose focus and include both important applications as well as a deep theoretical analysis of Artificial Neural Networks to control discrete-time nonlinear systems. It is important to remark that first, the considered Artificial Neural Networks are development in discrete-time this simplify its implementation in real-time; secondly, the proposed applications ranging from modelling of unknown discrete-time on linear systems to control electrical machines with an emphasize to renewable energy systems. However, its applications are not limited to these kind of systems, due to their theoretical foundation it can be applicable to a large class of nonlinear systems. All of these is supported by the solid research done by the author.'— Alma Y. Alanis, University of Guadalajara, Mexico'This book discusses in detail; how neural networks can be used for optimal as well as robust control design. Design of neural network controllers for real time applications such as induction motors, boost converters, inverted pendulum and doubly fed induction generators has also been carried out which gives the book an edge over other similar titles. This book will be an asset for the novice to the experienced ones.'— Rajesh Joseph Abraham, Indian Institute of Space Science & Technology, Thiruvananthapuram, India

Nonlinear Control of Robots and Unmanned Aerial Vehicles: An Integrated Approach presents control and regulation methods that rely upon feedback linearization techniques. Both robot manipulators and UAVs employ operating regimes with large magnitudes of state and control variables, making such an approach vital for their control systems design. Numerous application examples are included to facilitate the art of nonlinear control system design, for both robotic systems and UAVs, in a single unified framework. MATLAB® and Simulink® are integrated to demonstrate the importance of computational methods and systems simulation in this process.

The inverted pendulum is a classic problem in dynamics and control theory and is widely used as a benchmark for testing control algorithms. It is also an area of active study, with many new innovations and applications - for example the problem is solved in the technology of the Segway, a self-balancing transportation device.

Reset control is concerned with how to reset a system when it is disturbed to overcome the inherent limitations of linear feedback control and to improve robustness. It has found applications in many practical systems including flexible mechanical systems, tapespeed control systems and high precision positioning systems.

Due to its abilities to compensate disturbances and uncertainties, disturbance observer based control (DOBC) is regarded as one of the most promising approaches for disturbance-attenuation. One of the first books on DOBC, Disturbance Observer Based Control: Methods and Applications presents novel theory results as well as best practices for applica

Discrete-Time Inverse Optimal Control for Nonlinear Systems proposes a novel inverse optimal control scheme for stabilization and trajectory tracking of discrete-time nonlinear systems. This avoids the need to solve the associated Hamilton-Jacobi-Bellman equation and minimizes a cost functional, resulting in a more efficient controller.Design More Efficient Controllers for Stabilization and Trajectory Tracking of Discrete-Time Nonlinear SystemsThe book presents two approaches for controller synthesis: the first based on passivity theory and the second on a control Lyapunov function (CLF). The synthesized discrete-time optimal controller can be directly implemented in real-time systems. The book also proposes the use of recurrent neural networks to model discrete-time nonlinear systems. Combined with the inverse optimal control approach, such models constitute a powerful tool to deal with uncertainties such as unmodeled dynamics and disturbances.Learn from Simulations and an In-Depth Case StudyThe authors include a variety of simulations to illustrate the effectiveness of the synthesized controllers for stabilization and trajectory tracking of discrete-time nonlinear systems. An in-depth case study applies the control schemes to glycemic control in patients with type 1 diabetes mellitus, to calculate the adequate insulin delivery rate required to prevent hyperglycemia and hypoglycemia levels.The discrete-time optimal and robust control techniques proposed can be used in a range of industrial applications, from aerospace and energy to biomedical and electromechanical systems. Highlighting optimal and efficient control algorithms, this is a valuable resource for researchers, engineers, and students working in nonlinear system control.

Frequency-Domain Control Design for High-Performance Systems serves as a practical guide for the control engineer, and attempts to bridge the gap between industrial and academic control theory. Frequency-domain techniques rooted in classical control theory are presented with new approaches in nonlinear compensation that result in robust, high-performance closed-loop systems. Illustrative examples using data from actual control designs are included.

Underwater vehicles present some difficult and very particular control system design problems. These are often the result of nonlinear dynamics and uncertain models, as well as the presence of sometimes unforeseeable environmental disturbances that are difficult to measure or estimate. Autonomous Underwater Vehicles: Modeling, Control Design, and Simulation outlines a novel approach to help readers develop models to simulate feedback controllers for motion planning and design. The book combines useful information on both kinematic and dynamic nonlinear feedback control models, providing simulation results and other essential information, giving readers a truly unique and all-encompassing new perspective on design.Includes MATLAB® Simulations to Illustrate Concepts and Enhance UnderstandingStarting with an introductory overview, the book offers examples of underwater vehicle construction, exploring kinematic fundamentals, problem formulation, and controllability, among other key topics. Particularly valuable to researchers is the book's detailed coverage of mathematical analysis as it applies to controllability, motion planning, feedback, modeling, and other concepts involved in nonlinear control design. Throughout, the authors reinforce the implicit goal in underwater vehicle design—to stabilize and make the vehicle follow a trajectory precisely.Fundamentally nonlinear in nature, the dynamics of AUVs present a difficult control system design problem which cannot be easily accommodated by traditional linear design methodologies. The results presented here can be extended to obtain advanced control strategies and design schemes not only for autonomous underwater vehicles but also for other similar problems in the area of nonlinear control.

This book provides the most important steps and concerns in the design of estimation and control algorithms for induction motors. A single notation and modern nonlinear control terminology is used to make the book accessible, although a more theoretical control viewpoint is also given. Focusing on the induction motor with, the concepts of stability and nonlinear control theory given in appendices, this book covers: speed sensorless control; design of adaptive observers and parameter estimators; a discussion of nonlinear adaptive controls containing parameter estimation algorithms; and comparative simulations of different control algorithms. The book sets out basic assumptions, structural properties, modelling, state feedback control and estimation algorithms, then moves to more complex output feedback control algorithms, based on stator current measurements, and modelling for speed sensorless control. The induction motor exhibits many typical and unavoidable nonlinear features.

Intelligent systems are a hallmark of modern feedback control systems. But as these systems mature, we have come to expect higher levels of performance in speed and accuracy in the face of severe nonlinearities, disturbances, unforeseen dynamics, and unstructured uncertainties. Artificial neural networks offer a combination of adaptability, parallel processing, and learning capabilities that outperform other intelligent control methods in more complex systems.Borrowing from BiologyExamining neurocontroller design in discrete-time for the first time, Neural Network Control of Nonlinear Discrete-Time Systems presents powerful modern control techniques based on the parallelism and adaptive capabilities of biological nervous systems. At every step, the author derives rigorous stability proofs and presents simulation examples to demonstrate the concepts.Progressive DevelopmentAfter an introduction to neural networks, dynamical systems, control of nonlinear systems, and feedback linearization, the book builds systematically from actuator nonlinearities and strict feedback in nonlinear systems to nonstrict feedback, system identification, model reference adaptive control, and novel optimal control using the Hamilton-Jacobi-Bellman formulation. The author concludes by developing a framework for implementing intelligent control in actual industrial systems using embedded hardware.Neural Network Control of Nonlinear Discrete-Time Systems fosters an understanding of neural network controllers and explains how to build them using detailed derivations, stability analysis, and computer simulations.

Model-based predictive control (MPC) has proved to be a fertile area of research. It has gained enormous success within industry, especially in the context of process control. Nonlinear model-based predictive control (NMPC) is of particular interest as this best represents the dynamics of most real plant. This book collects together the important results which have emerged in this field, illustrating examples by means of simulations on industrial models. In particular there are contributions on feedback linearisation, differential flatness, control Lyapunov functions, output feedback, and neural networks. The international contributors to the book are all respected leaders within the field, which makes for essential reading for advanced students, researchers and industrialists in the field of control of complex systems.