Sammenlign metoder

	Model Predictive Control	Adaptiv regulering
Fagområde	Reguleringsteknik	Reguleringsteknik
Familie	Machine learning	Machine learning
Oprindelsesår	1978	1983
Ophavsperson	Jacques Richalet	Karl J. Astrom
Type	algorithm	algorithm
Oprindelig kilde	Richalet, J., Rault, A., Testud, J., & Papon, J. (1978). Model predictive heuristic control. Automatica, 14(5), 413-428.	Astrom, K. J., & Wittenmark, B. (1983). Computer-Controlled Systems: Theory and Design. Prentice Hall.
Aliasser	MPC, Receding Horizon Control	Self-Tuning Control, Parameter Estimation Control
Relaterede	5	3
Resumé	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.	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.