Comparar métodos

	Controle por Rejeição Ativa de Perturbações	Controle Adaptativo	Controle Preditivo por Modelo
Área	Teoria de controle	Teoria de controle	Teoria de controle
Família	Machine learning	Machine learning	Machine learning
Ano de origem	2009	1983	1978
Autor original	Jingquan Han	Karl J. Astrom	Jacques Richalet
Tipo	algorithm	algorithm	algorithm
Fonte seminal	Han, J. (2009). From PID to active disturbance rejection control. IEEE Transactions on Industrial Electronics, 56(3), 900-906.	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.
Outros nomes	ADRC, Disturbance Rejection Control	Self-Tuning Control, Parameter Estimation Control	MPC, Receding Horizon Control
Relacionados	2	3	5
Resumo	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.	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.