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| Regulator Kwadraturowo-Liniowy (LQR)× | Regulator liniowo-kwadratowy× | |
|---|---|---|
| Dziedzina | Teoria sterowania | Teoria sterowania |
| Rodzina | Machine learning | Machine learning |
| Rok powstania | 1960 | 1960 |
| Twórca | Rudolf Kalman | Rudolf Kalman |
| Typ | algorithm | algorithm |
| Źródło pierwotne≠ | Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35-45. DOI ↗ | Kalman, R. E. (1960). Contributions to the theory of optimal control. Boletin de la Sociedad Matematica Mexicana, 5(2), 102-119. link ↗ |
| Inne nazwy | LQG, LQR with Kalman Filter | LQR, Linear Quadratic Optimal Control |
| Pokrewne≠ | 3 | 4 |
| Podsumowanie≠ | The Linear Quadratic Gaussian (LQG) controller combines the Linear Quadratic Regulator (LQR) with a Kalman Filter to handle stochastic systems with measurement noise and process noise. Developed by Kalman and later formalized by Athans and others, LQG is the natural stochastic extension of LQR and remains the gold standard for optimal linear control under noise, with applications spanning spacecraft, aircraft autopilot, and industrial process control. | 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. |
| ScholarGateZbiór danych ↗ |
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