Sammenlign metoder

	Direkte Drejningsmomentkontrol	Adaptiv regulering	Model Predictive Control
Fagområde	Reguleringsteknik	Reguleringsteknik	Reguleringsteknik
Familie	Machine learning	Machine learning	Machine learning
Oprindelsesår	1986	1983	1978
Ophavsperson	Isao Takahashi	Karl J. Astrom	Jacques Richalet
Type	algorithm	algorithm	algorithm
Oprindelig kilde	Takahashi, I., & Noguchi, T. (1986). A new quick-response and high-efficiency control strategy of an induction motor. IEEE Transactions on Industry Applications, IA-22(5), 820-827.	Astrom, K. J., & Wittenmark, B. (1983). Computer-Controlled Systems: Theory and Design. Prentice Hall.	Richalet, J., Rault, A., Testud, J., & Papon, J. (1978). Model predictive heuristic control. Automatica, 14(5), 413-428.
Aliasser	DTC, Direct Flux Control	Self-Tuning Control, Parameter Estimation Control	MPC, Receding Horizon Control
Relaterede	3	3	5
Resumé	Direct Torque Control (DTC) is a method for controlling induction motors by directly manipulating magnetic flux and torque through switching of power converter inverter arms. Introduced by Takahashi and Noguchi in 1986, DTC provides fast torque response, low harmonic distortion, and robust performance without requiring current controllers or coordinate transformations, making it ideal for high-performance drive applications.	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.	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.