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Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Régulateur Linéaire Quadratique× | Équation de Hamilton-Jacobi-Bellman× | |
|---|---|---|
| Domaine | Théorie du contrôle | Théorie du contrôle |
| Famille | Machine learning | Machine learning |
| Année d'origine≠ | 1960 | 1957 |
| Auteur d'origine≠ | Rudolf Kalman | Richard Bellman |
| Type | algorithm | algorithm |
| Source fondatrice≠ | Kalman, R. E. (1960). Contributions to the theory of optimal control. Boletin de la Sociedad Matematica Mexicana, 5(2), 102-119. link ↗ | Bellman, R. (1957). Dynamic Programming. Princeton University Press. link ↗ |
| Alias≠ | LQR, Linear Quadratic Optimal Control | HJB Equation, Bellman Equation, Dynamic Programming |
| Apparentées≠ | 4 | 3 |
| Résumé≠ | The Linear Quadratic Regulator (LQR) is a classical optimal control algorithm that computes a linear feedback law to minimize a quadratic cost function for a linear dynamical system. Introduced by Kalman in 1960, LQR provides a provably optimal, closed-form solution for linear systems and remains fundamental in control theory, robotics, and aerospace applications because of its theoretical elegance and computational efficiency. | 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. |
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