Machine learningOptimal Control
Linear Quadratic Regulator
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.
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Sources
- Kalman, R. E. (1960). Contributions to the theory of optimal control. Boletin de la Sociedad Matematica Mexicana, 5(2), 102-119. link ↗
- Bryson, A. E., & Ho, Y. C. (1969). Applied Optimal Control: Optimization, Estimation and Control. Blaisdell Publishing. link ↗
- Lewis, F. L., Vrabie, D., & Syrmos, V. L. (2012). Optimal Control (3rd ed.). John Wiley & Sons. DOI: 10.1002/9781118122624 ↗