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| Linear Quadratic Regulator× | Pontryagin-Maximumprinzip× | |
|---|---|---|
| Fachgebiet | Regelungstechnik | Regelungstechnik |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 1960 | 1962 |
| Urheber≠ | Rudolf Kalman | Lev Pontryagin |
| Typ | algorithm | algorithm |
| Wegweisende Quelle≠ | Kalman, R. E. (1960). Contributions to the theory of optimal control. Boletin de la Sociedad Matematica Mexicana, 5(2), 102-119. link ↗ | Pontryagin, L. S., Boltyanskii, V. G., Gamkrelidze, R. V., & Mischenko, E. F. (1962). The Mathematical Theory of Optimal Processes. John Wiley & Sons. link ↗ |
| Aliasnamen≠ | LQR, Linear Quadratic Optimal Control | PMP, Optimal Control, Costate Method |
| Verwandt≠ | 4 | 3 |
| Zusammenfassung≠ | 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 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. |
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