state_estim.md

Functions: StateEstimator Internals

Pages = ["state_estim.md"]

Estimator Construction

ModelPredictiveControl.init_estimstoch
ModelPredictiveControl.init_integrators
ModelPredictiveControl.augment_model
ModelPredictiveControl.init_ukf
ModelPredictiveControl.init_internalmodel
ModelPredictiveControl.init_predmat_mhe
ModelPredictiveControl.relaxarrival
ModelPredictiveControl.relaxX̂
ModelPredictiveControl.relaxŴ
ModelPredictiveControl.relaxV̂
ModelPredictiveControl.init_matconstraint_mhe
ModelPredictiveControl.get_optim_functions(::MovingHorizonEstimator, ::ModelPredictiveControl.GenericModel)

Augmented Model

ModelPredictiveControl.f̂!
ModelPredictiveControl.ĥ!

Update Quadratic Optimization

ModelPredictiveControl.initpred!(::MovingHorizonEstimator, ::LinModel)
ModelPredictiveControl.linconstraint!(::MovingHorizonEstimator, ::LinModel)

Solve Optimization Problem

ModelPredictiveControl.optim_objective!(::MovingHorizonEstimator)
ModelPredictiveControl.set_warmstart_mhe!
ModelPredictiveControl.predict_mhe!
ModelPredictiveControl.con_nonlinprog_mhe!

Remove Operating Points

ModelPredictiveControl.remove_op!

Init Estimate

ModelPredictiveControl.init_estimate!

Correct Estimate

ModelPredictiveControl.correct_estimate!

Update Estimate

!!! info All these methods assume that the u0, y0m and d0 arguments are deviation vectors from their respective operating points (see setop!). The associated equations in the documentation drops the \mathbf{0} in subscript to simplify the notation. Strictly speaking, the manipulated inputs, measured outputs, measured disturbances and estimated states should be denoted with \mathbf{u_0, y_0^m, d_0} and \mathbf{x̂_0}, respectively.

ModelPredictiveControl.update_estimate!