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| Regulator liniowo-kwadratowy× | Sterowanie predykcyjne oparte na modelu× | |
|---|---|---|
| Dziedzina | Teoria sterowania | Teoria sterowania |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 1960 | 1978 |
| Twórca≠ | Rudolf Kalman | Jacques Richalet |
| Typ | algorithm | algorithm |
| Źródło pierwotne≠ | Kalman, R. E. (1960). Contributions to the theory of optimal control. Boletin de la Sociedad Matematica Mexicana, 5(2), 102-119. link ↗ | Richalet, J., Rault, A., Testud, J., & Papon, J. (1978). Model predictive heuristic control. Automatica, 14(5), 413-428. DOI ↗ |
| Inne nazwy | LQR, Linear Quadratic Optimal Control | MPC, Receding Horizon Control |
| Pokrewne≠ | 4 | 5 |
| Podsumowanie≠ | 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. | Model Predictive Control (MPC) is an advanced control strategy that uses an explicit process model to predict future system behavior over a finite horizon and solves an optimization problem at each control step. First formalized by Richalet et al. in 1978, MPC has become the dominant approach in process control industries, from chemical plants to autonomous vehicles, because it naturally handles constraints and can optimize multiple objectives simultaneously. |
| ScholarGateZbiór danych ↗ |
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