JordanModelDecomposition[ss] yields the Jordan decomposition of a StateSpaceModel object ss. The result is a list {s, jc} where s is a similarity matrix and jc is the Jordan ...
StateFeedbackGains[ss, {p_1, p_2, ..., p_n}] gives the state feedback gain matrix for the StateSpaceModel object ss such that the poles of the closed-loop system are p_i.
NicholsGridLines is an option to NicholsPlot that specifies contours of constant magnitude and constant phase of the closed-loop system.
LQGRegulator[{ss, sensors, finputs}, {w, v, h}, {q, r, p}] constructs the optimal feedback regulator for the StateSpaceModel ss using noisy measurements sensors and feedback ...
Long used in its simplest form in mathematics, functional iteration is an elegant way to represent repeated operations. Mathematica's symbolic architecture makes powerful ...
LQEstimatorGains[ss, {w, v}] gives the optimal estimator gain matrix for the StateSpaceModel object ss with process and measurement noise covariance matrices w and ...
TransferFunctionModel[m, var] represents the model of the transfer-function matrix m with complex variable var.TransferFunctionModel[{num, den}, var] specifies the numerator ...
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 ...
BodePlot[g] gives the Bode plot of a rational function g in one complex variable.BodePlot[sys] gives the Bode plot of a TransferFunctionModel or StateSpaceModel object ...
NicholsPlot[g] gives the Nichols plot of a rational function g in one complex variable.NicholsPlot[sys] gives the Nichols plot of a TransferFunctionModel or StateSpaceModel ...