Porovnat metody

	Adaptivní řízení	Iterative Learning Control	Modelové prediktivní řízení
Obor	Teorie řízení	Teorie řízení	Teorie řízení
Rodina	Machine learning	Machine learning	Machine learning
Rok vzniku	1983	1984	1978
Tvůrce	Karl J. Astrom	Suguru Arimoto	Jacques Richalet
Typ	algorithm	algorithm	algorithm
Původní zdroj	Astrom, K. J., & Wittenmark, B. (1983). Computer-Controlled Systems: Theory and Design. Prentice Hall.	Arimoto, S., Kawamura, S., & Miyazaki, F. (1984). Bettering operation of robots by learning. Journal of Robotic Systems, 1(2), 123-140.	Richalet, J., Rault, A., Testud, J., & Papon, J. (1978). Model predictive heuristic control. Automatica, 14(5), 413-428.
Další názvy	Self-Tuning Control, Parameter Estimation Control	ILC, Learning Control, Repetitive Control	MPC, Receding Horizon Control
Příbuzné	3	4	5
Shrnutí	Adaptive Control is a control strategy that adjusts controller parameters in real-time based on online system identification to maintain performance despite changing plant dynamics or uncertain parameters. Pioneered by Astrom and Wittenmark, adaptive control enables robust operation in time-varying environments, from aircraft with fuel depletion to industrial systems with aging components.	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.	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.