Methoden vergleichen

	H-infinity-Regelung	Linear Quadratic Regulator
Fachgebiet	Regelungstechnik	Regelungstechnik
Familie	Machine learning	Machine learning
Entstehungsjahr	1981	1960
Urheber	George Zames	Rudolf Kalman
Typ	algorithm	algorithm
Wegweisende Quelle	Zames, G. (1981). Feedback and optimal sensitivity: Model reference transformations, multiplicative seminorms, and approximate inverses. IEEE Transactions on Automatic Control, 26(2), 301-320.	Kalman, R. E. (1960). Contributions to the theory of optimal control. Boletin de la Sociedad Matematica Mexicana, 5(2), 102-119.
Aliasnamen	H∞ Control, Robust Control, Minimax Control	LQR, Linear Quadratic Optimal Control
Verwandt	4	4
Zusammenfassung	H-infinity (H∞) control is a robust control method that minimizes the worst-case gain from disturbances to controlled outputs, formulated as a minimax optimization problem. Pioneered by Zames in the early 1980s, H∞ control provides a principled way to design feedback controllers that tolerate model uncertainty, unmodeled dynamics, and disturbances while maintaining stability and performance, making it essential for applications requiring guaranteed robustness.	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.