Uncertain Optimal Control
Uncertain Optimal Control

Author: Yuanguo Zhu

Publisher: Springer

Published: 2018-08-29

Total Pages: 211

ISBN-13: 9811321345

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This book introduces the theory and applications of uncertain optimal control, and establishes two types of models including expected value uncertain optimal control and optimistic value uncertain optimal control. These models, which have continuous-time forms and discrete-time forms, make use of dynamic programming. The uncertain optimal control theory relates to equations of optimality, uncertain bang-bang optimal control, optimal control with switched uncertain system, and optimal control for uncertain system with time-delay. Uncertain optimal control has applications in portfolio selection, engineering, and games. The book is a useful resource for researchers, engineers, and students in the fields of mathematics, cybernetics, operations research, industrial engineering, artificial intelligence, economics, and management science.

Uncertain Models and Robust Control
Uncertain Models and Robust Control

Author: Alexander Weinmann

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 699

ISBN-13: 3709167116

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This coherent introduction to the theory and methods of robust control system design clarifies and unifies the presentation of significant derivations and proofs. The book contains a thorough treatment of important material of uncertainties and robust control otherwise scattered throughout the literature.

Robust and Optimal Control
Robust and Optimal Control

Author: Kemin Zhou

Publisher: Pearson

Published: 1996

Total Pages: 0

ISBN-13: 9780134565675

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Class-tested at major institutions around the world, this work offers complete coverage of robust and H control. It features clear coverage of methodology, and provides detailed treatment of topics including Riccati equations, m theory, H loopshaping and controller reduction.

Optimal Control
Optimal Control

Author: Arturo Locatelli

Publisher: Springer Science & Business Media

Published: 2001-03

Total Pages: 318

ISBN-13: 9783764364083

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From the reviews: "The style of the book reflects the author’s wish to assist in the effective learning of optimal control by suitable choice of topics, the mathematical level used, and by including numerous illustrated examples. . . .In my view the book suits its function and purpose, in that it gives a student a comprehensive coverage of optimal control in an easy-to-read fashion." —Measurement and Control

Optimal Control
Optimal Control

Author: Brian D. O. Anderson

Publisher: Courier Corporation

Published: 2007-02-27

Total Pages: 465

ISBN-13: 0486457664

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Numerous examples highlight this treatment of the use of linear quadratic Gaussian methods for control system design. It explores linear optimal control theory from an engineering viewpoint, with illustrations of practical applications. Key topics include loop-recovery techniques, frequency shaping, and controller reduction. Numerous examples and complete solutions. 1990 edition.

Optimal and Robust Control
Optimal and Robust Control

Author: Luigi Fortuna

Publisher: CRC Press

Published: 2012-02-02

Total Pages: 253

ISBN-13: 146650191X

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While there are many books on advanced control for specialists, there are few that present these topics for nonspecialists. Assuming only a basic knowledge of automatic control and signals and systems, Optimal and Robust Control: Advanced Topics with MATLAB® offers a straightforward, self-contained handbook of advanced topics and tools in automatic control. Techniques for Controlling System Performance in the Presence of Uncertainty The book deals with advanced automatic control techniques, paying particular attention to robustness—the ability to guarantee stability in the presence of uncertainty. It explains advanced techniques for handling uncertainty and optimizing the control loop. It also details analytical strategies for obtaining reduced order models. The authors then propose using the Linear Matrix Inequalities (LMI) technique as a unifying tool to solve many types of advanced control problems. Topics covered include: LQR and H-infinity approaches Kalman and singular value decomposition Open-loop balancing and reduced order models Closed-loop balancing Passive systems and bounded-real systems Criteria for stability control This easy-to-read text presents the essential theoretical background and provides numerous examples and MATLAB exercises to help the reader efficiently acquire new skills. Written for electrical, electronic, computer science, space, and automation engineers interested in automatic control, this book can also be used for self-study or for a one-semester course in robust control.

Stochastic Controls
Stochastic Controls

Author: Jiongmin Yong

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 459

ISBN-13: 1461214661

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As is well known, Pontryagin's maximum principle and Bellman's dynamic programming are the two principal and most commonly used approaches in solving stochastic optimal control problems. * An interesting phenomenon one can observe from the literature is that these two approaches have been developed separately and independently. Since both methods are used to investigate the same problems, a natural question one will ask is the fol lowing: (Q) What is the relationship betwccn the maximum principlc and dy namic programming in stochastic optimal controls? There did exist some researches (prior to the 1980s) on the relationship between these two. Nevertheless, the results usually werestated in heuristic terms and proved under rather restrictive assumptions, which were not satisfied in most cases. In the statement of a Pontryagin-type maximum principle there is an adjoint equation, which is an ordinary differential equation (ODE) in the (finite-dimensional) deterministic case and a stochastic differential equation (SDE) in the stochastic case. The system consisting of the adjoint equa tion, the original state equation, and the maximum condition is referred to as an (extended) Hamiltonian system. On the other hand, in Bellman's dynamic programming, there is a partial differential equation (PDE), of first order in the (finite-dimensional) deterministic case and of second or der in the stochastic case. This is known as a Hamilton-Jacobi-Bellman (HJB) equation.

Flexible and Generalized Uncertainty Optimization
Flexible and Generalized Uncertainty Optimization

Author: Weldon A. Lodwick

Publisher: Springer Nature

Published: 2021-01-12

Total Pages: 201

ISBN-13: 3030611809

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This book presents the theory and methods of flexible and generalized uncertainty optimization. Particularly, it describes the theory of generalized uncertainty in the context of optimization modeling. The book starts with an overview of flexible and generalized uncertainty optimization. It covers uncertainties that are both associated with lack of information and are more general than stochastic theory, where well-defined distributions are assumed. Starting from families of distributions that are enclosed by upper and lower functions, the book presents construction methods for obtaining flexible and generalized uncertainty input data that can be used in a flexible and generalized uncertainty optimization model. It then describes the development of the associated optimization model in detail. Written for graduate students and professionals in the broad field of optimization and operations research, this second edition has been revised and extended to include more worked examples and a section on interval multi-objective mini-max regret theory along with its solution method.

Optimization and Control for Partial Differential Equations
Optimization and Control for Partial Differential Equations

Author: Roland Herzog

Publisher: Walter de Gruyter GmbH & Co KG

Published: 2022-03-07

Total Pages: 474

ISBN-13: 3110695987

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This book highlights new developments in the wide and growing field of partial differential equations (PDE)-constrained optimization. Optimization problems where the dynamics evolve according to a system of PDEs arise in science, engineering, and economic applications and they can take the form of inverse problems, optimal control problems or optimal design problems. This book covers new theoretical, computational as well as implementation aspects for PDE-constrained optimization problems under uncertainty, in shape optimization, and in feedback control, and it illustrates the new developments on representative problems from a variety of applications.