Сравнение методов

	Линейный квадратичный регулятор	Модельно-прогнозирующее управление
Область	Теория управления	Теория управления
Семейство	Machine learning	Machine learning
Год появления	1960	1978
Автор метода	Rudolf Kalman	Jacques Richalet
Тип	algorithm	algorithm
Основополагающий источник	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.
Другие названия	LQR, Linear Quadratic Optimal Control	MPC, Receding Horizon Control
Связанные	4	5
Сводка	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.