विधियों की तुलना करें

	डायरेक्ट टॉर्क कंट्रोल	मॉडल प्रेडिक्टिव कंट्रोल
क्षेत्र	नियंत्रण सिद्धांत	नियंत्रण सिद्धांत
परिवार	Machine learning	Machine learning
उद्भव वर्ष	1986	1978
प्रवर्तक	Isao Takahashi	Jacques Richalet
प्रकार	algorithm	algorithm
मौलिक स्रोत	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.
उपनाम	DTC, Direct Flux Control	MPC, Receding Horizon Control
संबंधित	3	5
सारांश	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.