State-space control design methods, such as LQG/LQR and pole-placement algorithms, are useful for MIMO design.
|Linear-Quadratic Regulator (LQR) design|
|Form linear-quadratic (LQ) state-feedback regulator with output weighting|
|Linear-quadratic (LQ) state-feedback regulator for discrete-time state-space system|
|Design discrete linear-quadratic (LQ) regulator for continuous plant|
|Linear-Quadratic-Gaussian (LQG) design|
|Form linear-quadratic-Gaussian (LQG) regulator|
|Form Linear-Quadratic-Gaussian (LQG) servo controller|
|Append state vector to output vector|
|Norm of linear model|
Linear-quadratic-Gaussian (LQG) control is a state-space technique that allows you to trade off regulation/tracker performance and control effort, and to take into account process disturbances and measurement noise.
Use linear-quadratic-Gaussian techniques to regulate the beam thickness in a steel rolling mill.
This case study illustrates the classical design process.
Design an LQG regulator for a plant output in a system with noise.
Design an LQG servo controller using a Kalman state estimator.
Design an LQR controller for a system modeled in Simulink®.
Closed-loop pole locations have a direct impact on time response characteristics such as rise time, settling time, and transient oscillations. Pole placement uses state-space techniques to assign closed-loop poles.