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Optimization and optimal control in a nutshell

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Optimization and optimal control in a nutshellby Sudath Rohan Munasinghe.
作者:	Munasinghe, Sudath Rohan.
出版者:	Singapore :Springer Nature Singapore :2024.
面頁冊數:	xvii, 127 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Mathematical optimization.
電子資源:	https://doi.org/10.1007/978-981-97-8167-6
ISBN:	9789819781676$q(electronic bk.)

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$q(electronic bk.)

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

183292
Mathematical optimization.

LC Class. No.: QA402.5

Dewey Class. No.: 519.6

Optimization and optimal control in a nutshell
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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.
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