مقایسهٔ روش‌ها

	کنترل پیش‌بین مدل	تنظیم‌کننده خطی-درجه‌دوم (Linear Quadratic Regulator)
حوزه	نظریه کنترل	نظریه کنترل
خانواده	Machine learning	Machine learning
سال پیدایش	1978	1960
پدیدآور	Jacques Richalet	Rudolf Kalman
نوع	algorithm	algorithm
منبع بنیادین	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.
نام‌های دیگر	MPC, Receding Horizon Control	LQR, Linear Quadratic Optimal Control
مرتبط	5	4
خلاصه	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.