Comparar métodos

	Controle H-infinito	Controle Preditivo por Modelo
Área	Teoria de controle	Teoria de controle
Família	Machine learning	Machine learning
Ano de origem	1981	1978
Autor original	George Zames	Jacques Richalet
Tipo	algorithm	algorithm
Fonte seminal	Zames, G. (1981). Feedback and optimal sensitivity: Model reference transformations, multiplicative seminorms, and approximate inverses. IEEE Transactions on Automatic Control, 26(2), 301-320.	Richalet, J., Rault, A., Testud, J., & Papon, J. (1978). Model predictive heuristic control. Automatica, 14(5), 413-428.
Outros nomes	H∞ Control, Robust Control, Minimax Control	MPC, Receding Horizon Control
Relacionados	4	5
Resumo	H-infinity (H∞) control is a robust control method that minimizes the worst-case gain from disturbances to controlled outputs, formulated as a minimax optimization problem. Pioneered by Zames in the early 1980s, H∞ control provides a principled way to design feedback controllers that tolerate model uncertainty, unmodeled dynamics, and disturbances while maintaining stability and performance, making it essential for applications requiring guaranteed robustness.	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.