While many books cover the theory of optimal design, few help readers to actually apply it. this volume is one of the first to aid readers in utilizing the theory of optimal control to solve practical problems in the face of uncertainty. Bryson covers random inputs and random errors in measurement, uncertainty in inputs from the environment, and uncertainty in the parameters of the dynamic model. He also addresses static and dynamic estimation, random processes, several types of controllers, smoothers, and filters. Pedagogic features include hundreds of problems and worked examples, a CD-ROM supplement containing MATLAB codes of the OPTEST toolbox, and code for the examples, figures, and many of the problems.
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Editorial Reviews
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Applied Linear Optimal Control successfully fills the gap between the theory of optimal control design and practical applications in the face of uncertainties. The student should find this book to be an invaluable supplemental tool combined with a solid theoretical framework in the classroom. The practicing engineer should find this book to be an invaluable reference tool in understanding the issues involved with practical control design. Both the student and the practicing engineer will especially appreciate the several examples backed up with MATLAB codes, which serve to provide more interesting problems than the book's predecessor. This book should be a valuable addition to an individual's collection or to an institution's library." American Institute of Aeronautics and Astronautics Journal
"Applied Linear Optimal Control: Examples and Algorithms should be a good addition to the optimal control community. It makes an excellent text for engineering and applied mathematics students who already have some optimal control background. It is strongly recommended as a helpful guide for anyone who desires to learn and apply many of the current state of the art results in optimal control." Applied Mechanics Reviews
"Applied Linear Optimal Control: Examples and Algorithms should be a good addition to the optimal control community. It makes an excellent text for engineering and applied mathematics students who already have some optimal control background. It is strongly recommended as a helpful guide for anyone who desires to learn and apply many of the current state of the art results in optimal control." Applied Mechanics Reviews
Book Description
This book is one of the first to aid readers in utilizing the theory of optimal control to solve practical problems in the face of uncertainty. Topics include random inputs and random errors in measurement, uncertainty in inputs, uncertainty in the parameters of the dynamic model, static and dynamic estimation, random processes, and several types of controllers. Hundreds of problems and worked examples enhance the pedagogy, and a CD-ROM supplement contains MATLAB codes of the OPTEST toolbox, as well as code for the examples, figures, and many of the problems within the text.
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This review is from: Applied Linear Optimal Control: Examples and Algorithms (Paperback)
If you are looking for a book explaining practical optimal control computation stuffs, this is the book.
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Inside This Book
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First Sentence:
A random scalar variable x is described completely by its density function p(x), where p(x(0) dx is the probability that x will take on values between x(0) and x(0) + dx (see Figure 1.1). Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
symmetric root locus, reciprocal root locus, covariance matrix propagate, lateral specific force, plant parameter space, step force input, linearized lateral equations, step output command, noisy sensor outputs, pursuer acceleration, thrust specific force, smoother problem, forward covariance, likelihood ellipse, colored measurement noise, intercept guidance, covariance filter, triple inverted pendulum, steady forward flight, purely random sequence, follower logic, random forcing functions, parameter margin, random vector sequence, white measurement noise Key Phrases - Capitalized Phrases (CAPs): (learn more)
Applied Linear Optimal Control, Helicopter Position Change, Two-Mass-Spring Plant Using, Position Estimate Error, Velocity Estimate Error, First-Order Follower, Monte Carlo, Discrete Oscillator, Glide-Slope Hold Autopilot, Linear-Quadratic State-Feedback Follower-Controllers, Consider Example, Helicopter Acceleration Command Using, Optimization Toolbox, Unit Distance, Autoland of Boeing, Dynamic Estimation, Phase Gain Margin Param New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
A random scalar variable x is described completely by its density function p(x), where p(x(0) dx is the probability that x will take on values between x(0) and x(0) + dx (see Figure 1.1). Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
symmetric root locus, reciprocal root locus, covariance matrix propagate, lateral specific force, plant parameter space, step force input, linearized lateral equations, step output command, noisy sensor outputs, pursuer acceleration, thrust specific force, smoother problem, forward covariance, likelihood ellipse, colored measurement noise, intercept guidance, covariance filter, triple inverted pendulum, steady forward flight, purely random sequence, follower logic, random forcing functions, parameter margin, random vector sequence, white measurement noise Key Phrases - Capitalized Phrases (CAPs): (learn more)
Applied Linear Optimal Control, Helicopter Position Change, Two-Mass-Spring Plant Using, Position Estimate Error, Velocity Estimate Error, First-Order Follower, Monte Carlo, Discrete Oscillator, Glide-Slope Hold Autopilot, Linear-Quadratic State-Feedback Follower-Controllers, Consider Example, Helicopter Acceleration Command Using, Optimization Toolbox, Unit Distance, Autoland of Boeing, Dynamic Estimation, Phase Gain Margin Param New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 14
books:
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cites this book:
- Applied Optimal Control: Optimization, Estimation and Control by Jr. Arthur E. Bryson in Front Matter, and Back Matter
- Kalman Filtering: Theory and Practice by Mohinder S. Grewal in Back Matter
- Aircraft Dynamics and Automatic Control by Duane McRuer in Back Matter
- Linear Estimation by Thomas Kailath in Back Matter
- Neural Network Systems Techniques and Applications, Seven-Volume Set: Control and Dynamic Systems, Neural Network Systems Techniques and Applications, Volume 7 by Cornelius T. Leondes in Back Matter
See all 14 books this book cites
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