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Prezrite si vybrané metódy vedľa seba; riadky, ktoré sa líšia, sú zvýraznené.
| Regulácia H-infinity× | Regulátor s lineárnou kvadratickou funkciou (LQR)× | |
|---|---|---|
| Odbor | Teória riadenia | Teória riadenia |
| Rodina | Machine learning | Machine learning |
| Rok vzniku≠ | 1981 | 1960 |
| Tvorca≠ | George Zames | Rudolf Kalman |
| Typ | algorithm | algorithm |
| Pôvodný zdroj≠ | Zames, G. (1981). Feedback and optimal sensitivity: Model reference transformations, multiplicative seminorms, and approximate inverses. IEEE Transactions on Automatic Control, 26(2), 301-320. DOI ↗ | Kalman, R. E. (1960). Contributions to the theory of optimal control. Boletin de la Sociedad Matematica Mexicana, 5(2), 102-119. link ↗ |
| Ďalšie názvy≠ | H∞ Control, Robust Control, Minimax Control | LQR, Linear Quadratic Optimal Control |
| Príbuzné | 4 | 4 |
| Zhrnutie≠ | H-infinity (H∞) control is a robust control method that minimizes the worst-case gain from disturbances to controlled outputs, formulated as a minimax optimization problem. Pioneered by Zames in the early 1980s, H∞ control provides a principled way to design feedback controllers that tolerate model uncertainty, unmodeled dynamics, and disturbances while maintaining stability and performance, making it essential for applications requiring guaranteed robustness. | 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. |
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