Methoden vergleichen

	Modellprädiktive Regelung	Linear Quadratic Regulator
Fachgebiet	Regelungstechnik	Regelungstechnik
Familie	Machine learning	Machine learning
Entstehungsjahr	1978	1960
Urheber	Jacques Richalet	Rudolf Kalman
Typ	algorithm	algorithm
Wegweisende Quelle	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.
Aliasnamen	MPC, Receding Horizon Control	LQR, Linear Quadratic Optimal Control
Verwandt	5	4
Zusammenfassung	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.