Sammenlign metoder

	Linear Quadratic Regulator	Model Predictive Control
Fagområde	Reguleringsteknik	Reguleringsteknik
Familie	Machine learning	Machine learning
Oprindelsesår	1960	1978
Ophavsperson	Rudolf Kalman	Jacques Richalet
Type	algorithm	algorithm
Oprindelig kilde	Kalman, R. E. (1960). Contributions to the theory of optimal control. Boletin de la Sociedad Matematica Mexicana, 5(2), 102-119.	Richalet, J., Rault, A., Testud, J., & Papon, J. (1978). Model predictive heuristic control. Automatica, 14(5), 413-428.
Aliasser	LQR, Linear Quadratic Optimal Control	MPC, Receding Horizon Control
Relaterede	4	5
Resumé	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.