Comparar métodos

	Controle Preditivo por Modelo	Controle Adaptativo
Área	Teoria de controle	Teoria de controle
Família	Machine learning	Machine learning
Ano de origem	1978	1983
Autor original	Jacques Richalet	Karl J. Astrom
Tipo	algorithm	algorithm
Fonte seminal	Richalet, J., Rault, A., Testud, J., & Papon, J. (1978). Model predictive heuristic control. Automatica, 14(5), 413-428.	Astrom, K. J., & Wittenmark, B. (1983). Computer-Controlled Systems: Theory and Design. Prentice Hall.
Outros nomes	MPC, Receding Horizon Control	Self-Tuning Control, Parameter Estimation Control
Relacionados	5	3
Resumo	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.	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.