Confronta i metodi

	Controllo a Rifiuto Attivo dei Disturbi	Controllo Predittivo Basato su Modello
Campo	Teoria del controllo	Teoria del controllo
Famiglia	Machine learning	Machine learning
Anno di origine	2009	1978
Ideatore	Jingquan Han	Jacques Richalet
Tipo	algorithm	algorithm
Fonte seminale	Han, J. (2009). From PID to active disturbance rejection control. IEEE Transactions on Industrial Electronics, 56(3), 900-906.	Richalet, J., Rault, A., Testud, J., & Papon, J. (1978). Model predictive heuristic control. Automatica, 14(5), 413-428.
Alias	ADRC, Disturbance Rejection Control	MPC, Receding Horizon Control
Correlati	2	5
Sintesi	Active Disturbance Rejection Control (ADRC) is a control method that estimates and cancels disturbances and model uncertainties in real-time using an extended state observer (ESO), treating them as additional 'disturbance states'. Developed by Han and popularized by Gao, ADRC achieves remarkable robustness without requiring precise plant models, making it practical for real-world systems with significant uncertainty and disturbances.	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.