পদ্ধতির তুলনা করুন

	রৈখিক দ্বিঘাত গাউসীয়	লিনিয়ার কোয়াড্রেটিক রেগুলেটর (LQR)
ক্ষেত্র	নিয়ন্ত্রণ তত্ত্ব	নিয়ন্ত্রণ তত্ত্ব
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	1960	1960
প্রবর্তক	Rudolf Kalman	Rudolf Kalman
ধরন	algorithm	algorithm
মৌলিক উৎস	Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35-45.	Kalman, R. E. (1960). Contributions to the theory of optimal control. Boletin de la Sociedad Matematica Mexicana, 5(2), 102-119.
অপর নাম	LQG, LQR with Kalman Filter	LQR, Linear Quadratic Optimal Control
সম্পর্কিত	3	4
সারসংক্ষেপ	The Linear Quadratic Gaussian (LQG) controller combines the Linear Quadratic Regulator (LQR) with a Kalman Filter to handle stochastic systems with measurement noise and process noise. Developed by Kalman and later formalized by Athans and others, LQG is the natural stochastic extension of LQR and remains the gold standard for optimal linear control under noise, with applications spanning spacecraft, aircraft autopilot, and industrial process control.	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.