Sammenlign metoder

	Adaptiv regulering	Feltorienteret Styring	Model Predictive Control
Fagområde	Reguleringsteknik	Reguleringsteknik	Reguleringsteknik
Familie	Machine learning	Machine learning	Machine learning
Oprindelsesår	1983	1972	1978
Ophavsperson	Karl J. Astrom	Flemming Blaschke	Jacques Richalet
Type	algorithm	algorithm	algorithm
Oprindelig kilde	Astrom, K. J., & Wittenmark, B. (1983). Computer-Controlled Systems: Theory and Design. Prentice Hall.	Blaschke, F. (1972). The principle of field orientation as applied to the new transvector closed-loop control system for rotating field machines. Siemens Review, 34(5), 217-220.	Richalet, J., Rault, A., Testud, J., & Papon, J. (1978). Model predictive heuristic control. Automatica, 14(5), 413-428.
Aliasser	Self-Tuning Control, Parameter Estimation Control	FOC, Vector Control	MPC, Receding Horizon Control
Relaterede	3	2	5
Resumé	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.	Field-Oriented Control (FOC), also known as Vector Control, is an advanced method for controlling AC induction and permanent magnet motors by decomposing phase currents into torque and flux components and independently regulating them using PI controllers. Pioneered by Blaschke in 1972, FOC enables smooth precise motor control equivalent to DC motor performance, making it the standard for high-performance industrial variable-speed drives.	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.