Nonlinear control

All practical systems contain nonlinear dynamics. Control system development for these systems has traditionally been based on linearized system dynamics in conjunction with linear control techniques. Sometimes it is possible to describe the operation of systems by a linear model around its operating points. Linearized system can provide approximate behavior of the system. But in analyzing the overall system behavior, the resulting system model is inadequate or inaccurate. Moreover, the stability of the system cannot be guaranteed.
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Fuzzy control is a practical alternative for a variety of challenging control applications since it provides a convenient method for constructing nonlinear controllers via the use of heuristic information. Such heuristic information may come from an operator who has acted as a “humanintheloop” controller for a process. In the fuzzy control design methodology, we ask this operator to write down a set of rules on how to control the process, then we incorporate these into a fuzzy controller that emulates the decisionmaking process of the human.
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A trend of inverstigation of Nonlinear Control Systems has been present over the last few decades. As a result the methods for its analysis and design have improved rapidly. This book includes nonlinear design topics such as Feedback Linearization, Lyapunov Based Control, Adaptive Control, Optimal Control and Robust Control.
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Typical dynamic responses are also shown. The motor is initially at standstill and at no load when a step command in speed is applied; when steadystate conditions are reached, a reversal of speed is commanded followed by a step load application. The system is highly nonlinear due to the introduction of saturation needed to limit both the current delivered and the voltage applied to the motor.
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Automation is the use of control systems (such as numerical control, programmable logic control, and other industrial control systems), in concert with other applications of information technology (such as computeraided technologies [CAD, CAM, CAx]), to control industrial machinery and processes, reducing the need for human intervention. In the scope of industrialization, automation is a step beyond mechanization.
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This book is a compilation of some selected articles devoted to the analysis and control of vibrations. Vibrations are a phenomenon found in many engineering systems; their harmful effects are translated into low performance, noise, energy misspend, discomfort and system breakdown, among others. These are the reasons why, in the last years, researchers have made great efforts in seeking ways to eliminate them totally or partially.
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The current control problems present natural trend of increasing its complexity due to performance criteria that is becoming more sophisticated. The necessity of practicers and engineers in dealing with complex dynamic systems has motivated the design of controllers, whose structures are based on multiobjective constraints, knowledge from expert, uncertainties, nonlinearities, parameters that vary with time, time delay conditions, multivariable systems, and others.
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The current control problems present natural trend of increasing its complexity due to performance criteria that is becoming more sophisticated. The necessity of practicers and engineers in dealing with complex dynamic systems has motivated the design of controllers, whose structures are based on multiobjective constraints, knowledge from expert, uncertainties, nonlinearities, parameters that vary with time, time delay conditions, multivariable systems, and others.
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Nonlinear problems in fl ight control have stimulated cooperation among engineers and scientists from a range of disciplines. Developments in computer technology allowed for numerical solutions of nonlinear control problems, while industrial recognition and applications of nonlinear mathematical models in solving technological problems is increasing.
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A Series of Reference Books and Textbooks Editors NEIL MUNRO, PH.D., D.SC. Professor Applied Control Engineering University of Manchester Institute of Science and Technology Manchester, United Kingdom FRANK L.LEWIS, PH.D. MoncriefO’Donnell Endowed Chair and Associate Director of Research Automation & Robotics Research Institute University of Texas, Arlington 1. Nonlinear Control of Electric Machinery, Darren M.Dawson, Jun Hu, and Timothy C.Burg 2. Computational Intelligence in Control Engineering, Robert E.King 3.
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The book is intended to provide an introduction to the effects of nonlinear elements in feedback control systems. A central topic is the use of the Describing Function (DF) method since in combination with simulation it provides an excellent approach for the practicing engineer and follows on logically from a first course in classical control, such as the companion volume in this series. Some of the basic material on the topic can be found in my earlier book which is frequently referenced throughout the text....
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This lecture will teach you how to fit nonlinear functions by using bases functions and how to control model complexity. The goal is for you to: Learn how to derive ridge regression; understand the tradeoff of fitting the data and regularizing it; Learn polynomial regression; understand that, if basis functions are given, the problem of learning the parameters is still linear; learn crossvalidation; understand model complexity and generalization.
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Lecture "Fundamentals of control systems  Chapter 2: Mathematical models of continuous control systems" presentation of content: Presentation of content, transfer function, block diagram algebra, signal flow diagram, state space equation, linearized models of nonlinear systems.
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The aim of the dissertation is study and propose a new algorithm for solving the optimal problem in nonlinear model predictive control MIMO system.
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The book is concerned with the effects of nonlinearity in feedback control systems and techniques which can be used to design feedback loops containing nonlinear elements. After a short introductory chapter on nonlinearity and its possible effects the use of phase plane methods for nonlinear second order systems is discussed.
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Electric motors convert electrical energy into mechanical energy by utilizing the properties of electromagnetic energy conversion. The different types of motors operate in different ways and have different methods of calculating the performance, but all utilize some arrangement of magnetic fields. Understanding the concepts of electromagnetics and the systems of units that are employed is essential to understanding electric motor operation.The first part of this chapter covers the concepts and units and shows how forces are developed.
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Evolution of global technologies has prompted increasing complexity of applications developed in both, the industry and the scientific research fields. These complexities are generally attributed to nonlinearities, poorly defined dynamics and absence of apriori information about the systems. Imprecision, uncertainties and vagueness in information about the system are also playing vital roles in enhancing the complexity of application.
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GeneralPurpose SlidingMode Control Implementation Conclusions Switchmode power supplies represent a particular class of variable structure systems (VSS), and they can take advantage of nonlinear control techniques
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TS FUZZY MODEL AS UNIVERSAL APPROXIMATOR In this chapter, we present two results concerning the fuzzy modeling and control of nonlinear systems w1x. First, we prove that any smooth nonlinear control systems can be approximated by TakagiSugeno fuzzy models with linear rule consequence. Then, we prove that any smooth nonlinear state feedback controller can be approximated by the parallel distributed compensation ŽPDC. controller. Among various fuzzy modeling themes, the TakagiSugeno ŽTS. model w2x has been one of the most popular modeling frameworks....
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The aim of this book is to provide an overview of recent developments in Kalman filter theory and their applications in engineering and scientific fields. The book is divided into 24 chapters and organized in five blocks corresponding to recent advances in Kalman filtering theory, applications in medical and biological sciences, tracking and positioning systems, electrical engineering and, finally, industrial processes and communication networks.
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