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 ...

Mathematica provides powerful functions to compute state-feedback and estimator gains using pole-placement or optimal techniques. In addition, it has functions that directly ...

EstimatorRegulator[ss, {l, \[Kappa]}] constructs the feedback regulator for the StateSpaceModel object ss with estimator and feedback gain matrices l and \[Kappa], ...

SystemsModelStateFeedbackConnect[ss, controller] gives the closed-loop system for the StateSpaceModel object ss with state feedback controller ...

DiscreteLQRegulatorGains[ss, {q, r}, \[Tau]] gives the optimal discrete-time state feedback gain matrix with sampling period \[Tau] for the continuous-time StateSpaceModel ...

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 ...

LQOutputRegulatorGains[ss, {q, r}] gives the optimal state feedback gain matrix for the StateSpaceModel object ss and the quadratic cost function with output and control ...

RiccatiSolve[{a, b}, {q, r}] gives the matrix x that is the stabilizing solution of the continuous algebraic Riccati equation ConjugateTranspose[a].x + x.a - ...

DiscreteRiccatiSolve[{a, b}, {q, r}] gives the matrix x that is the stabilizing solution of the discrete algebraic Riccati equation ConjugateTranspose[a].x.a - x - ...