Linear-quadratic-Gaussian control combines a linear system, Gaussian uncertainty, a quadratic cost, and feedback based on a Kalman filter state estimate.
In a linear-quadratic-Gaussian control problem, the optimal control applies the full-information linear-quadratic feedback law to the conditional state estimate. The control gain is independent of the state-estimation error covariance.
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Linear–quadratic–Gaussian (LQG) control is a mathematical framework used in control theory that combines three key elements: 1. **Linear Dynamics**: The system being controlled is modeled using linear differential or difference equations. This means that the system's behavior can be described by linear relationships, allowing for a straightforward analysis and control design.