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	Sterowanie predykcyjne oparte na modelu	Regulator liniowo-kwadratowy
Dziedzina	Teoria sterowania	Teoria sterowania
Rodzina	Machine learning	Machine learning
Rok powstania	1978	1960
Twórca	Jacques Richalet	Rudolf Kalman
Typ	algorithm	algorithm
Źródło pierwotne	Richalet, J., Rault, A., Testud, J., & Papon, J. (1978). Model predictive heuristic control. Automatica, 14(5), 413-428.	Kalman, R. E. (1960). Contributions to the theory of optimal control. Boletin de la Sociedad Matematica Mexicana, 5(2), 102-119.
Inne nazwy	MPC, Receding Horizon Control	LQR, Linear Quadratic Optimal Control
Pokrewne	5	4
Podsumowanie	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.	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.