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_223
100 1 _aMunasinghe, Sudath Rohan.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aOptimization and Optimal Control in a Nutshell
_h[electronic resource] /
_cby Sudath Rohan Munasinghe.
250 _a1st ed. 2024.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2024.
300 _aXVII, 127 p. 45 illus., 34 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aEngineering Optimization: Methods and Applications,
_x2731-4057
505 0 _a1. 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 _aThis 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.
541 _fUABC ;
_cPerpetuidad
650 0 _aControl engineering.
650 0 _aRobotics.
650 0 _aAutomation.
650 0 _aDiscrete mathematics.
650 0 _aMathematical optimization.
650 1 4 _aControl, Robotics, Automation.
650 2 4 _aDiscrete Mathematics.
650 2 4 _aOptimization.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789819781669
776 0 8 _iPrinted edition:
_z9789819781683
776 0 8 _iPrinted edition:
_z9789819781690
830 0 _aEngineering Optimization: Methods and Applications,
_x2731-4057
856 4 0 _zLibro electrónico
_uhttp://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-981-97-8167-6
912 _aZDB-2-INR
912 _aZDB-2-SXIT
942 _cLIBRO_ELEC
999 _c276899
_d276898