Comparar métodos

	Princípio do Máximo de Pontryagin	Controle Preditivo por Modelo
Área	Teoria de controle	Teoria de controle
Família	Machine learning	Machine learning
Ano de origem	1962	1978
Autor original	Lev Pontryagin	Jacques Richalet
Tipo	algorithm	algorithm
Fonte seminal	Pontryagin, L. S., Boltyanskii, V. G., Gamkrelidze, R. V., & Mischenko, E. F. (1962). The Mathematical Theory of Optimal Processes. John Wiley & Sons.	Richalet, J., Rault, A., Testud, J., & Papon, J. (1978). Model predictive heuristic control. Automatica, 14(5), 413-428.
Outros nomes	PMP, Optimal Control, Costate Method	MPC, Receding Horizon Control
Relacionados	3	5
Resumo	The Pontryagin Maximum Principle (PMP) is a fundamental theorem in optimal control theory providing necessary conditions for optimality of a control trajectory. Published by Lev Pontryagin in 1962, PMP generalizes the calculus of variations to control problems with constraints and is the theoretical foundation enabling solution of complex trajectory optimization problems from spacecraft missions to industrial process optimization.	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.