Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Lineární kvadratický regulátor× | Pontryaginův princip maxima× | |
|---|---|---|
| Obor | Teorie řízení | Teorie řízení |
| Rodina | Machine learning | Machine learning |
| Rok vzniku≠ | 1960 | 1962 |
| Tvůrce≠ | Rudolf Kalman | Lev Pontryagin |
| Typ | algorithm | algorithm |
| Původní zdroj≠ | Kalman, R. E. (1960). Contributions to the theory of optimal control. Boletin de la Sociedad Matematica Mexicana, 5(2), 102-119. link ↗ | Pontryagin, L. S., Boltyanskii, V. G., Gamkrelidze, R. V., & Mischenko, E. F. (1962). The Mathematical Theory of Optimal Processes. John Wiley & Sons. link ↗ |
| Další názvy≠ | LQR, Linear Quadratic Optimal Control | PMP, Optimal Control, Costate Method |
| Příbuzné≠ | 4 | 3 |
| Shrnutí≠ | The Linear Quadratic Regulator (LQR) is a classical optimal control algorithm that computes a linear feedback law to minimize a quadratic cost function for a linear dynamical system. Introduced by Kalman in 1960, LQR provides a provably optimal, closed-form solution for linear systems and remains fundamental in control theory, robotics, and aerospace applications because of its theoretical elegance and computational efficiency. | The Pontryagin Maximum Principle (PMP) is a fundamental theorem in optimal control theory providing necessary conditions for optimality of a control trajectory. Published by Lev Pontryagin in 1962, PMP generalizes the calculus of variations to control problems with constraints and is the theoretical foundation enabling solution of complex trajectory optimization problems from spacecraft missions to industrial process optimization. |
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