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	Sterowanie predykcyjne oparte na modelu	Równanie Hamiltona-Jacobiego-Bellmana
Dziedzina	Teoria sterowania	Teoria sterowania
Rodzina	Machine learning	Machine learning
Rok powstania	1978	1957
Twórca	Jacques Richalet	Richard Bellman
Typ	algorithm	algorithm
Źródło pierwotne	Richalet, J., Rault, A., Testud, J., & Papon, J. (1978). Model predictive heuristic control. Automatica, 14(5), 413-428.	Bellman, R. (1957). Dynamic Programming. Princeton University Press.
Inne nazwy	MPC, Receding Horizon Control	HJB Equation, Bellman Equation, Dynamic Programming
Pokrewne	5	3
Podsumowanie	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.	The Hamilton-Jacobi-Bellman (HJB) equation is a partial differential equation characterizing the optimal cost-to-go function in dynamic programming. Developed by Bellman in 1957, HJB provides both necessary and sufficient conditions for optimality, enabling elegant theoretical analysis and numerical solutions for optimal control problems. HJB is fundamental to reinforcement learning, approximate dynamic programming, and real-time control.