Direct Adaptive Control Algorithms

Direct Adaptive Control Algorithms

Author: Howard Kaufman

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 445

ISBN-13: 146120657X

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Suitable either as a reference for practising engineers or as a text for a graduate course in adaptive control systems, this is a self-contained compendium of readily implementable adaptive control algorithms. These algorithms have been developed and applied by the authors for over fifteen years to a wide variety of engineering problems including flexible structure control, blood pressure control, and robotics. As such, they are suitable for a wide variety of multiple input-output control systems with uncertainty and external disturbances. The text is intended to enable anyone with knowledge of basic linear multivariable systems to adapt the algorithms to problems in a wide variety of disciplines. Thus, in addition to developing the theoretical details of the algorithms presented, the text gives considerable emphasis to designing algorithms and to representative applications in flight control, flexible structure control, robotics, and drug-infusion control. This second edition makes good use of MATLAB programs for the illustrative examples; these programs are described in the text and can be obtained from the MathWorks file server.


Robust Adaptive Control

Robust Adaptive Control

Author: Petros Ioannou

Publisher: Courier Corporation

Published: 2013-09-26

Total Pages: 850

ISBN-13: 0486320723

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Presented in a tutorial style, this comprehensive treatment unifies, simplifies, and explains most of the techniques for designing and analyzing adaptive control systems. Numerous examples clarify procedures and methods. 1995 edition.


Learning-Based Adaptive Control

Learning-Based Adaptive Control

Author: Mouhacine Benosman

Publisher: Butterworth-Heinemann

Published: 2016-08-02

Total Pages: 284

ISBN-13: 0128031514

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Adaptive control has been one of the main problems studied in control theory. The subject is well understood, yet it has a very active research frontier. This book focuses on a specific subclass of adaptive control, namely, learning-based adaptive control. As systems evolve during time or are exposed to unstructured environments, it is expected that some of their characteristics may change. This book offers a new perspective about how to deal with these variations. By merging together Model-Free and Model-Based learning algorithms, the author demonstrates, using a number of mechatronic examples, how the learning process can be shortened and optimal control performance can be reached and maintained. - Includes a good number of Mechatronics Examples of the techniques. - Compares and blends Model-free and Model-based learning algorithms. - Covers fundamental concepts, state-of-the-art research, necessary tools for modeling, and control.


Adaptive Control Systems

Adaptive Control Systems

Author: Gang Feng

Publisher: Newnes

Published: 1999-06-08

Total Pages: 360

ISBN-13: 9780750639965

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List of contributors; Preface; Adaptive internal model control; An algorithm for robust adaptive control with less prior knowledge; Adaptive variable structure control; Indirect adaptive periodic control; Adaptive stabilization of uncertain discrete-time systems via switching control: the method of localization; Adaptive nonlinear control: passivation and small gain techniques; Active identification for control of discrete-time uncertain nonlinear systems; Optimal adaptive tracking for nonlinear systems; Stable adaptive systems in the presence of nonlinear parametrization; Adaptive inverse for actuator compensation; Stable multi-input multi-output adaptive fuzzy/neural control; Adaptive robust control scheme with an application to PM synchronous motors; Index.


System Identification and Adaptive Control

System Identification and Adaptive Control

Author: Yiannis Boutalis

Publisher: Springer Science & Business

Published: 2014-04-23

Total Pages: 316

ISBN-13: 3319063642

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Presenting current trends in the development and applications of intelligent systems in engineering, this monograph focuses on recent research results in system identification and control. The recurrent neurofuzzy and the fuzzy cognitive network (FCN) models are presented. Both models are suitable for partially-known or unknown complex time-varying systems. Neurofuzzy Adaptive Control contains rigorous proofs of its statements which result in concrete conclusions for the selection of the design parameters of the algorithms presented. The neurofuzzy model combines concepts from fuzzy systems and recurrent high-order neural networks to produce powerful system approximations that are used for adaptive control. The FCN model stems from fuzzy cognitive maps and uses the notion of “concepts” and their causal relationships to capture the behavior of complex systems. The book shows how, with the benefit of proper training algorithms, these models are potent system emulators suitable for use in engineering systems. All chapters are supported by illustrative simulation experiments, while separate chapters are devoted to the potential industrial applications of each model including projects in: • contemporary power generation; • process control and • conventional benchmarking problems. Researchers and graduate students working in adaptive estimation and intelligent control will find Neurofuzzy Adaptive Control of interest both for the currency of its models and because it demonstrates their relevance for real systems. The monograph also shows industrial engineers how to test intelligent adaptive control easily using proven theoretical results.


Nonlinear and Adaptive Control with Applications

Nonlinear and Adaptive Control with Applications

Author: Alessandro Astolfi

Publisher: Springer Science & Business Media

Published: 2007-12-06

Total Pages: 302

ISBN-13: 1848000669

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The authors here provide a detailed treatment of the design of robust adaptive controllers for nonlinear systems with uncertainties. They employ a new tool based on the ideas of system immersion and manifold invariance. New algorithms are delivered for the construction of robust asymptotically-stabilizing and adaptive control laws for nonlinear systems. The methods proposed lead to modular schemes that are easier to tune than their counterparts obtained from Lyapunov redesign.


Adaptive Control Design and Analysis

Adaptive Control Design and Analysis

Author: Gang Tao

Publisher: John Wiley & Sons

Published: 2003-07-09

Total Pages: 652

ISBN-13: 9780471274520

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A systematic and unified presentation of the fundamentals of adaptive control theory in both continuous time and discrete time Today, adaptive control theory has grown to be a rigorous and mature discipline. As the advantages of adaptive systems for developing advanced applications grow apparent, adaptive control is becoming more popular in many fields of engineering and science. Using a simple, balanced, and harmonious style, this book provides a convenient introduction to the subject and improves one's understanding of adaptive control theory. Adaptive Control Design and Analysis features: Introduction to systems and control Stability, operator norms, and signal convergence Adaptive parameter estimation State feedback adaptive control designs Parametrization of state observers for adaptive control Unified continuous and discrete-time adaptive control L1+a robustness theory for adaptive systems Direct and indirect adaptive control designs Benchmark comparison study of adaptive control designs Multivariate adaptive control Nonlinear adaptive control Adaptive compensation of actuator nonlinearities End-of-chapter discussion, problems, and advanced topics As either a textbook or reference, this self-contained tutorial of adaptive control design and analysis is ideal for practicing engineers, researchers, and graduate students alike.


Direct Adaptive Control Algorithms:

Direct Adaptive Control Algorithms:

Author: Howard Kaufman

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 380

ISBN-13: 146840217X

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Suitable either as a reference or as a text for a graduate course in adaptive control systems, this book is a self-contained compendium of easily implementable adaptive control algorithms that have been developed and applied by the authors for over 10 years. These algorithms do not require explicit process parameter identification and have been successfully applied to a wide variety of engineering problems including flexible structure control, blood pressure control and robotics. In general, these algorithms are suitable for a wide class of multiple input-output control systems containing significant uncertainty as well as disturbances.


Robust and Adaptive Control

Robust and Adaptive Control

Author: Eugene Lavretsky

Publisher: Springer

Published: 2023-10-05

Total Pages: 0

ISBN-13: 9783031383137

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Robust and Adaptive Control (second edition) shows readers how to produce consistent and accurate controllers that operate in the presence of uncertainties and unforeseen events. Driven by aerospace applications, the focus of the book is primarily on continuous-time dynamical systems. The two-part text begins with robust and optimal linear control methods and moves on to a self-contained presentation of the design and analysis of model reference adaptive control for nonlinear uncertain dynamical systems. Features of the second edition include: sufficient conditions for closed-loop stability under output feedback observer-based loop-transfer recovery (OBLTR) with adaptive augmentation; OBLTR applications to aerospace systems; case studies that demonstrate the benefits of robust and adaptive control for piloted, autonomous and experimental aerial platforms; realistic examples and simulation data illustrating key features of the methods described; and problem solutions for instructors and MATLAB® code provided electronically. The theory and practical applications address real-life aerospace problems, being based on numerous transitions of control-theoretic results into operational systems and airborne vehicles drawn from the authors’ extensive professional experience with The Boeing Company. The systems covered are challenging—often open-loop unstable with uncertainties in their dynamics—and thus require both persistently reliable control and the ability to track commands either from a pilot or a guidance computer. Readers should have a basic understanding of root locus, Bode diagrams, and Nyquist plots, as well as linear algebra, ordinary differential equations, and the use of state-space methods in analysis and modeling of dynamical systems. The second edition contains a background summary of linear systems and control systems and an introduction to state observers and output feedback control, helping to make it self-contained. Robust and Adaptive Control teaches senior undergraduate and graduate students how to construct stable and predictable control algorithms for realistic industrial applications. Practicing engineers and academic researchers will also find the book of great instructional value.


Adaptive Control with Recurrent High-order Neural Networks

Adaptive Control with Recurrent High-order Neural Networks

Author: George A. Rovithakis

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 203

ISBN-13: 1447107853

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The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies ... , new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series offers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. Neural networks is one of those areas where an initial burst of enthusiasm and optimism leads to an explosion of papers in the journals and many presentations at conferences but it is only in the last decade that significant theoretical work on stability, convergence and robustness for the use of neural networks in control systems has been tackled. George Rovithakis and Manolis Christodoulou have been interested in these theoretical problems and in the practical aspects of neural network applications to industrial problems. This very welcome addition to the Advances in Industrial Control series provides a succinct report of their research. The neural network model at the core of their work is the Recurrent High Order Neural Network (RHONN) and a complete theoretical and simulation development is presented. Different readers will find different aspects of the development of interest. The last chapter of the monograph discusses the problem of manufacturing or production process scheduling.