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Optimization and optimal control in a nutshell

Record Type:	Electronic resources : Monograph/item
Title/Author:	Optimization and optimal control in a nutshell/ by Sudath Rohan Munasinghe.
Author:	Munasinghe, Sudath Rohan.
Published:	Singapore :Springer Nature Singapore : : 2024.,
Description:	xvii, 127 p. :ill., digital ;24 cm.
[NT 15003449]:	1. Unconstrained Function Optimization -- 2. Constrained Function Optimization -- 3. Unconstrained Functional Optimization -- 4. Constrained Functional Optimization -- 5. Continuous Time Optimal Control -- 6. Linear Quadratic Regulator -- 7. Optimal Control with Pontryagin's Maximum Principle -- 8. Discrete-time Optimal Control -- 9. Model Predictive Control.
Contained By:	Springer Nature eBook
Subject:	Control, Robotics, Automation. -
Online resource:	https://doi.org/10.1007/978-981-97-8167-6
ISBN:	9789819781676

Optimization and optimal control in a nutshell
Munasinghe, Sudath Rohan.

Optimization and optimal control in a nutshell[electronic resource] /by Sudath Rohan Munasinghe. - Singapore :Springer Nature Singapore :2024. - xvii, 127 p. :ill., digital ;24 cm. - Engineering optimization: methods and applications,2731-4057. - Engineering optimization: methods and applications..

1. Unconstrained Function Optimization -- 2. Constrained Function Optimization -- 3. Unconstrained Functional Optimization -- 4. Constrained Functional Optimization -- 5. Continuous Time Optimal Control -- 6. Linear Quadratic Regulator -- 7. Optimal Control with Pontryagin's Maximum Principle -- 8. Discrete-time Optimal Control -- 9. Model Predictive Control.

This book concisely presents the optimization process and optimal control process with examples and simulations to help self-learning and better comprehension. It starts with function optimization and constraint inclusion and then extends to functional optimization using the calculus of variations. The development of optimal controls for continuous-time, linear, open-loop systems is presented using Lagrangian and Pontryagin-Hamiltonian methods, showing how to introduce the end-point conditions in time and state. The closed-loop optimal control for linear systems with a quadratic cost function, well-known as the linear quadratic regulator (LQR) is developed for both time-bound and time-unbounded conditions. Some control systems need to maximize performance alongside cost minimization. The Pontryagin's maximum principle is presented in this regard with clear examples that show the practical implementation of it. It is shown through examples how the maximum principle leads to control switching and Bang-Bang control in certain types of systems. The application of optimal controls in discrete-time open-loop systems with the quadratic cost is presented and then extended to the closed-loop control, which results in the model predictive control (MPC). Throughout the book, examples and Matlab simulation codes are provided for the learner to practice the contents in each section. The aligned lineup of content helps the learner develop knowledge and skills in optimal control gradually and quickly.

ISBN: 9789819781676

Standard No.: 10.1007/978-981-97-8167-6doiSubjects--Topical Terms:

3592500
Control, Robotics, Automation.

LC Class. No.: QA402.5

Dewey Class. No.: 519.6

Optimization and optimal control in a nutshell
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245 1 0 $a Optimization and optimal control in a nutshell $h [electronic resource] / $c by Sudath Rohan Munasinghe.
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300 $a xvii, 127 p. : $b ill., digital ; $c 24 cm.
490 1 $a Engineering optimization: methods and applications, $x 2731-4057
505 0 $a 1. Unconstrained Function Optimization -- 2. Constrained Function Optimization -- 3. Unconstrained Functional Optimization -- 4. Constrained Functional Optimization -- 5. Continuous Time Optimal Control -- 6. Linear Quadratic Regulator -- 7. Optimal Control with Pontryagin's Maximum Principle -- 8. Discrete-time Optimal Control -- 9. Model Predictive Control.
520 $a This book concisely presents the optimization process and optimal control process with examples and simulations to help self-learning and better comprehension. It starts with function optimization and constraint inclusion and then extends to functional optimization using the calculus of variations. The development of optimal controls for continuous-time, linear, open-loop systems is presented using Lagrangian and Pontryagin-Hamiltonian methods, showing how to introduce the end-point conditions in time and state. The closed-loop optimal control for linear systems with a quadratic cost function, well-known as the linear quadratic regulator (LQR) is developed for both time-bound and time-unbounded conditions. Some control systems need to maximize performance alongside cost minimization. The Pontryagin's maximum principle is presented in this regard with clear examples that show the practical implementation of it. It is shown through examples how the maximum principle leads to control switching and Bang-Bang control in certain types of systems. The application of optimal controls in discrete-time open-loop systems with the quadratic cost is presented and then extended to the closed-loop control, which results in the model predictive control (MPC). Throughout the book, examples and Matlab simulation codes are provided for the learner to practice the contents in each section. The aligned lineup of content helps the learner develop knowledge and skills in optimal control gradually and quickly.
650 1 4 $a Control, Robotics, Automation. $3 3592500
650 2 4 $a Discrete Mathematics. $3 1569938
650 2 4 $a Optimization. $3 891104
650 0 $a Mathematical optimization. $3 517763
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950 $a Intelligent Technologies and Robotics (SpringerNature-42732)