Comparer des méthodes

	Commande itérative d'apprentissage	Linéarisation par retour
Domaine	Théorie du contrôle	Théorie du contrôle
Famille	Machine learning	Machine learning
Année d'origine	1984	1983
Auteur d'origine	Suguru Arimoto	Alberto Isidori
Type	algorithm	algorithm
Source fondatrice	Arimoto, S., Kawamura, S., & Miyazaki, F. (1984). Bettering operation of robots by learning. Journal of Robotic Systems, 1(2), 123-140.	Isidori, A. (1995). Nonlinear Control Systems (3rd ed.). Springer-Verlag.
Alias	ILC, Learning Control, Repetitive Control	Exact Linearization, Nonlinear Feedback Control, Input-Output Linearization
Apparentées	4	4
Résumé	Iterative Learning Control (ILC) is a control method for systems that perform the same task repeatedly (trajectory tracking over a fixed time interval). The key idea is to use error information from previous trials to update the input for the next trial, progressively improving tracking accuracy. Pioneered by Arimoto et al. in 1984, ILC is ideal for robotic manufacturing, semiconductor processing, and any application where the same motion must be repeated many times with high precision.	Feedback Linearization is a nonlinear control technique that uses a nonlinear state-feedback transformation to convert a nonlinear system into a linear one, enabling the use of standard linear control methods. Developed by Isidori, Sontag, and others in the 1980s, feedback linearization is conceptually elegant and powerful: if the system satisfies certain structural conditions (relative degree, decoupling matrix rank), the nonlinearities can be exactly cancelled through feedback, reducing the problem to linear design.