StateOutputEstimator[ss, l] constructs an estimator for the StateSpaceModel object ss with estimator gain matrix l.StateOutputEstimator[{ss, sensors}, l] uses only sensors as ...
LQRegulatorGains[ss, {q, r}] gives the optimal state feedback gain matrix for the StateSpaceModel object ss and the quadratic cost function with state and control weighting ...
EstimatorGains[ss, {p_1, p_2, ..., p_n}] gives the estimator gain matrix for the StateSpaceModel object ss, such that the poles of the estimator are p_i.
LyapunovSolve[a, c] finds a solution x of the matrix Lyapunov equation a.x + x.a\[ConjugateTranspose] == c.LyapunovSolve[a, b, c] solves a.x + x.b == c.LyapunovSolve[{a, d}, ...
DiscreteLQRegulatorGains[ss, {q, r}, \[Tau]] gives the optimal discrete-time state feedback gain matrix with sampling period \[Tau] for the continuous-time StateSpaceModel ...
KalmanEstimator[ss, {w, v}] constructs the Kalman estimator for the StateSpaceModel object ss with process and measurement noise covariance matrices w and v. ...
LQEstimatorGains[ss, {w, v}] gives the optimal estimator gain matrix for the StateSpaceModel object ss with process and measurement noise covariance matrices w and ...
InternallyBalancedDecomposition[ss] yields the internally balanced decomposition of the StateSpaceModel object ss. The result is a list {s, ib} where s is the similarity ...