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Optimal Design of Generalized Multiple Model Adaptive Controllers

Optimal Design of Generalized Multiple Model Adaptive Controllers
Author: Thomas E. Brehm
Publisher:
Total Pages: 298
Release: 2004-03-01
Genre: Adaptive control systems
ISBN: 9781423514862

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Advanced analysis and optimal design techniques that achieve performance improvement for multiple model adaptive control (MMAC) and multiple model adaptive estimation (MMAE) based control are developed and tested for this dissertation research. An adjunct area of research yielded modified linear quadratic Gaussian (LQG) control design techniques that also can be applied to nonadaptive control. For the Modified LQG (MLQG) controller, the proposed designs remove the assumption that the Kalman filter as the observer and the controller gain matrix design are necessarily based on the same model as the best system model. The filter and controller gain matrices are both determined by models possibly other than the system model. In order to achieve optimal performance, the interrelationship of the system model to the filter and controller design models is established by minimizing a position correlation (mean square error on output) measure. Enhanced robustness is realized by considering the performance over the range of values of specified parameter(s) of the system model.


Optimal Adaptive Control and Differential Games by Reinforcement Learning Principles

Optimal Adaptive Control and Differential Games by Reinforcement Learning Principles
Author: Draguna L. Vrabie
Publisher: IET
Total Pages: 305
Release: 2013
Genre: Computers
ISBN: 1849194890

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The book reviews developments in the following fields: optimal adaptive control; online differential games; reinforcement learning principles; and dynamic feedback control systems.


Model-Reference Adaptive Control

Model-Reference Adaptive Control
Author: Nhan T. Nguyen
Publisher: Springer
Total Pages: 453
Release: 2018-03-01
Genre: Technology & Engineering
ISBN: 3319563939

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This textbook provides readers with a good working knowledge of adaptive control theory through applications. It is intended for students beginning masters or doctoral courses, and control practitioners wishing to get up to speed in the subject expeditiously. Readers are taught a wide variety of adaptive control techniques starting with simple methods and extending step-by-step to more complex ones. Stability proofs are provided for all adaptive control techniques without obfuscating reader understanding with excessive mathematics. The book begins with standard model-reference adaptive control (MRAC) for first-order, second-order, and multi-input, multi-output systems. Treatment of least-squares parameter estimation and its extension to MRAC follow, helping readers to gain a different perspective on MRAC. Function approximation with orthogonal polynomials and neural networks, and MRAC using neural networks are also covered. Robustness issues connected with MRAC are discussed, helping the student to appreciate potential pitfalls of the technique. This appreciation is encouraged by drawing parallels between various aspects of robustness and linear time-invariant systems wherever relevant. Following on from the robustness problems is material covering robust adaptive control including standard methods and detailed exposition of recent advances, in particular, the author’s work on optimal control modification. Interesting properties of the new method are illustrated in the design of adaptive systems to meet stability margins. This method has been successfully flight-tested on research aircraft, one of various flight-control applications detailed towards the end of the book along with a hybrid adaptive flight control architecture that combines direct MRAC with least-squares indirect adaptive control. In addition to the applications, understanding is encouraged by the use of end-of-chapter exercises and associated MATLAB® files. Readers will need no more than the standard mathematics for basic control theory such as differential equations and matrix algebra; the book covers the foundations of MRAC and the necessary mathematical preliminaries.


Adaptive Optimal Control

Adaptive Optimal Control
Author: Robert R. Bitmead
Publisher:
Total Pages: 264
Release: 1990
Genre: Computers
ISBN:

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Exploring connections between adaptive control theory and practice, this book treats the techniques of linear quadratic optimal control and estimation (Kalman filtering), recursive identification, linear systems theory and robust arguments.


Adaptive Dual Control

Adaptive Dual Control
Author: Nikolai Michailovich Filatov
Publisher: Springer Science & Business Media
Total Pages: 258
Release: 2004-04-20
Genre: Technology & Engineering
ISBN: 9783540213734

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This monograph demonstrates how the performance of various well-known adaptive controllers can be improved significantly using the dual effect. The modifications to incorporate dual control are realized separately and independently of the main adaptive controller without complicating the algorithms. A new bicriterial approach for dual control is developed and applied to various types of popular linear and nonlinear adaptive controllers. Practical applications of the designed controllers to several real-time problems are presented. This monograph is the first book providing a complete exposition on the dual control problem from the inception in the early 1960s to the present state of the art aiming at students and researchers in adaptive control as well as design engineers in industry.


Cooperative Control of Multi-Agent Systems

Cooperative Control of Multi-Agent Systems
Author: Frank L. Lewis
Publisher: Springer Science & Business Media
Total Pages: 315
Release: 2013-12-31
Genre: Technology & Engineering
ISBN: 1447155742

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Cooperative Control of Multi-Agent Systems extends optimal control and adaptive control design methods to multi-agent systems on communication graphs. It develops Riccati design techniques for general linear dynamics for cooperative state feedback design, cooperative observer design, and cooperative dynamic output feedback design. Both continuous-time and discrete-time dynamical multi-agent systems are treated. Optimal cooperative control is introduced and neural adaptive design techniques for multi-agent nonlinear systems with unknown dynamics, which are rarely treated in literature are developed. Results spanning systems with first-, second- and on up to general high-order nonlinear dynamics are presented. Each control methodology proposed is developed by rigorous proofs. All algorithms are justified by simulation examples. The text is self-contained and will serve as an excellent comprehensive source of information for researchers and graduate students working with multi-agent systems.


Demonstration of the Optimal Control Modification for General Aviation

Demonstration of the Optimal Control Modification for General Aviation
Author: Scott Reed
Publisher:
Total Pages: 160
Release: 2010
Genre: Electronic dissertations
ISBN:

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This work presents the design and simulation of a model reference adaptive flight control system for general aviation. The controller is based on previous adaptive control research conducted at Wichita State University (WSU) and the National Aeronautics and Space Administration (NASA) Ames Research Center. The control system is designed for longitudinal control of a Beech Bonanza given the commands of pitch rate and airspeed. The structure of the controller includes a first-order model follower, proportional-integral (PI) controller, inverse controller, and adaptation element. Two adaptation methods were considered, the WSU-developed Adaptive Bias Corrector (ABC) and the Optimal Control Modification (OCM). The ABC is used with two error schemes, adapting to the modeling-error and the tracking-error. Three variations of the OCM are presented, which differ in the parameterization of the adaptive signal. The first is called OCM-Linear (OCM-L), where the adaptive signal is linearly related to the states. The second variation is OCM-Bias (OCM-B), which only includes a bias term. The third is the OCM-Linear and Bias (OCM-LB), a combination of the previous two variations. To design the controllers, varied values of the PI gains and adaptive gains were evaluated based on time response tracking of a pitch doublet and time delay margin. The time delay margin is based on error metrics developed at NASA Ames. Of the five controllers presented, the OCM-L and ABC with tracking-error adaptation performed the best. The ABC with modeling-error adaptation did not track the pitch doublet. The OCM-B and OCM-LB are good controllers but had worse performance than OCM-Linear in tracking and time delay margin, respectively.


Adaptive Control Design and Analysis

Adaptive Control Design and Analysis
Author: Gang Tao
Publisher: John Wiley & Sons
Total Pages: 652
Release: 2003-07-09
Genre: Science
ISBN: 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.


Model Free Adaptive Control

Model Free Adaptive Control
Author: Zhongsheng Hou
Publisher: CRC Press
Total Pages: 400
Release: 2013-09-24
Genre: Technology & Engineering
ISBN: 1466594187

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Model Free Adaptive Control: Theory and Applications summarizes theory and applications of model-free adaptive control (MFAC). MFAC is a novel adaptive control method for the unknown discrete-time nonlinear systems with time-varying parameters and time-varying structure, and the design and analysis of MFAC merely depend on the measured input and output data of the controlled plant, which makes it more applicable for many practical plants. This book covers new concepts, including pseudo partial derivative, pseudo gradient, pseudo Jacobian matrix, and generalized Lipschitz conditions, etc.; dynamic linearization approaches for nonlinear systems, such as compact-form dynamic linearization, partial-form dynamic linearization, and full-form dynamic linearization; a series of control system design methods, including MFAC prototype, model-free adaptive predictive control, model-free adaptive iterative learning control, and the corresponding stability analysis and typical applications in practice. In addition, some other important issues related to MFAC are also discussed. They are the MFAC for complex connected systems, the modularized controller designs between MFAC and other control methods, the robustness of MFAC, and the symmetric similarity for adaptive control system design. The book is written for researchers who are interested in control theory and control engineering, senior undergraduates and graduated students in engineering and applied sciences, as well as professional engineers in process control.