Compara mètodes

	Ajustament Ziegler-Nichols	Regulador Linear Quadràtic
Camp	Teoria de control	Teoria de control
Família	Machine learning	Machine learning
Any d'origen	1942	1960
Autor original	John G. Ziegler	Rudolf Kalman
Tipus	algorithm	algorithm
Font seminal	Ziegler, J. G., & Nichols, N. B. (1942). Optimum settings for automatic controllers. Transactions of the American Society of Mechanical Engineers, 64(8), 759-768.	Kalman, R. E. (1960). Contributions to the theory of optimal control. Boletin de la Sociedad Matematica Mexicana, 5(2), 102-119.
Àlies	PID Tuning, Empirical Tuning Method	LQR, Linear Quadratic Optimal Control
Relacionats	2	4
Resum	Ziegler-Nichols Tuning is a practical, model-free method for tuning PID controller gains empirically. Published in 1942, this pioneering method requires only measurement of the system's step response (or closed-loop oscillations), making it applicable to any system without prior identification. Ziegler-Nichols remains widely used in industry because it is simple, fast, and often produces reasonable initial tunings.	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.