পদ্ধতির তুলনা করুন

	লিনিয়ার কোয়াড্রেটিক রেগুলেটর (LQR)	মডেল প্রেডিকটিভ কন্ট্রোল
ক্ষেত্র	নিয়ন্ত্রণ তত্ত্ব	নিয়ন্ত্রণ তত্ত্ব
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	1960	1978
প্রবর্তক	Rudolf Kalman	Jacques Richalet
ধরন	algorithm	algorithm
মৌলিক উৎস	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.
অপর নাম	LQR, Linear Quadratic Optimal Control	MPC, Receding Horizon Control
সম্পর্কিত	4	5
সারসংক্ষেপ	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.