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Non-identifier Based Adaptive Control in Mechatronics

Non-identifier Based Adaptive Control in Mechatronics
Author: Christoph M. Hackl
Publisher: Springer
Total Pages: 652
Release: 2018-07-28
Genre: Technology & Engineering
ISBN: 9783319855493

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This book introduces non-identifier-based adaptive control (with and without internal model) and its application to the current, speed and position control of mechatronic systems such as electrical synchronous machines, wind turbine systems, industrial servo systems, and rigid-link, revolute-joint robots. In mechatronics, there is often only rough knowledge of the system. Due to parameter uncertainties, nonlinearities and unknown disturbances, model-based control strategies can reach their performance or stability limits without iterative controller design and performance evaluation, or system identification and parameter estimation. The non-identifier-based adaptive control presented is an alternative that neither identifies the system nor estimates its parameters but ensures stability. The adaptive controllers are easy to implement, compensate for disturbances and are inherently robust to parameter uncertainties and nonlinearities. For controller implementation only structural system knowledge (like relative degree, input-to-state stable zero dynamics and known sign of the high-frequency gain) is required. Moreover, the presented controllers guarantee reference tracking with prescribed asymptotic or transient accuracy, i.e. the tracking error eventually tends to or for all time evolves within an a priori specified region. The book presents the theory, modeling and application in a general but detailed and self-contained manner, making it easy to read and understand, particularly for newcomers to the topics covered


Adaptive Control

Adaptive Control
Author: Karl J. Åström
Publisher: Courier Corporation
Total Pages: 596
Release: 2013-04-26
Genre: Technology & Engineering
ISBN: 0486319148

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Suitable for advanced undergraduates and graduate students, this overview introduces theoretical and practical aspects of adaptive control, with emphasis on deterministic and stochastic viewpoints. 1995 edition.


Adaptive Control

Adaptive Control
Author: Shankar Sastry
Publisher: Courier Corporation
Total Pages: 402
Release: 2011-01-01
Genre: Technology & Engineering
ISBN: 0486482022

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This volume surveys the major results and techniques of analysis in the field of adaptive control. Focusing on linear, continuous time, single-input, single-output systems, the authors offer a clear, conceptual presentation of adaptive methods, enabling a critical evaluation of these techniques and suggesting avenues of further development. 1989 edition.


Theory of Self-Adaptive Control Systems

Theory of Self-Adaptive Control Systems
Author: P. H. Hammond
Publisher: Springer
Total Pages: 360
Release: 2013-11-11
Genre: Technology & Engineering
ISBN: 1489962891

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Robust and Adaptive Control

Robust and Adaptive Control
Author: Eugene Lavretsky
Publisher: Springer Science & Business Media
Total Pages: 506
Release: 2012-11-13
Genre: Technology & Engineering
ISBN: 1447143965

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Robust and Adaptive Control shows the reader 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-dynamical systems. The text is a three-part treatment, beginning with robust and optimal linear control methods and moving on to a self-contained presentation of the design and analysis of model reference adaptive control (MRAC) for nonlinear uncertain dynamical systems. Recent extensions and modifications to MRAC design are included, as are guidelines for combining robust optimal and MRAC controllers. Features of the text include: · case studies that demonstrate the benefits of robust and adaptive control for piloted, autonomous and experimental aerial platforms; · detailed background material for each chapter to motivate theoretical developments; · realistic examples and simulation data illustrating key features of the methods described; and · problem solutions for instructors and MATLAB® code provided electronically. The theoretical content and practical applications reported address real-life aerospace problems, being based on numerous transitions of control-theoretic results into operational systems and airborne vehicles that are 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 requiring both persistently reliable control and the ability to track commands either from a pilot or a guidance computer. Readers are assumed to 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. Robust and Adaptive Control is intended to methodically teach 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.


Stochastic Systems

Stochastic Systems
Author: P. R. Kumar
Publisher: SIAM
Total Pages: 371
Release: 2015-12-15
Genre: Mathematics
ISBN: 1611974259

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Since its origins in the 1940s, the subject of decision making under uncertainty has grown into a diversified area with application in several branches of engineering and in those areas of the social sciences concerned with policy analysis and prescription. These approaches required a computing capacity too expensive for the time, until the ability to collect and process huge quantities of data engendered an explosion of work in the area. This book provides succinct and rigorous treatment of the foundations of stochastic control; a unified approach to filtering, estimation, prediction, and stochastic and adaptive control; and the conceptual framework necessary to understand current trends in stochastic control, data mining, machine learning, and robotics.