Machine learningStochastic Control

Linear Quadratic Gaussian

The Linear Quadratic Gaussian (LQG) controller combines the Linear Quadratic Regulator (LQR) with a Kalman Filter to handle stochastic systems with measurement noise and process noise. Developed by Kalman and later formalized by Athans and others, LQG is the natural stochastic extension of LQR and remains the gold standard for optimal linear control under noise, with applications spanning spacecraft, aircraft autopilot, and industrial process control.

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Sources

  1. Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35-45. DOI: 10.1115/1.3662552
  2. Athans, M. (1971). The role and use of the stochastic linear-quadratic-gaussian problem in control system design. IEEE Transactions on Automatic Control, 16(6), 529-552. DOI: 10.1109/TAC.1971.1099818
  3. Kwakernaak, H., & Sivan, R. (1972). Linear Optimal Control Systems. Wiley-Interscience. link

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Referenced by

ScholarGateLinear Quadratic Gaussian (Linear Quadratic Gaussian). Retrieved 2026-06-04 from https://scholargate.app/en/control-theory/linear-quadratic-gaussian