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| Równanie Hamiltona-Jacobiego-Bellmana× | Sterowanie predykcyjne oparte na modelu× | |
|---|---|---|
| Dziedzina | Teoria sterowania | Teoria sterowania |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 1957 | 1978 |
| Twórca≠ | Richard Bellman | Jacques Richalet |
| Typ | algorithm | algorithm |
| Źródło pierwotne≠ | Bellman, R. (1957). Dynamic Programming. Princeton University Press. link ↗ | Richalet, J., Rault, A., Testud, J., & Papon, J. (1978). Model predictive heuristic control. Automatica, 14(5), 413-428. DOI ↗ |
| Inne nazwy≠ | HJB Equation, Bellman Equation, Dynamic Programming | MPC, Receding Horizon Control |
| Pokrewne≠ | 3 | 5 |
| Podsumowanie≠ | 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. | 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. |
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