השוואת שיטות

	בקרת שדה מכוונת	בקרת מומנט ישירה	בקרה מבוססת מודל חיזוי
תחום	תורת הבקרה	תורת הבקרה	תורת הבקרה
משפחה	Machine learning	Machine learning	Machine learning
שנת המקור	1972	1986	1978
הוגה השיטה	Flemming Blaschke	Isao Takahashi	Jacques Richalet
סוג	algorithm	algorithm	algorithm
מקור מכונן	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.	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.	Richalet, J., Rault, A., Testud, J., & Papon, J. (1978). Model predictive heuristic control. Automatica, 14(5), 413-428.
כינויים	FOC, Vector Control	DTC, Direct Flux Control	MPC, Receding Horizon Control
קשורות	2	3	5
תקציר	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.	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.	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.