debug tests

Author: franckgaga

src/controller/execute.jl

@@ -529,13 +529,14 @@ setstate!(mpc::PredictiveController, x̂) = (setstate!(mpc.estim, x̂); return m
 Set `model` and objective function weights of `mpc` [`PredictiveController`](@ref).
 
 Allows model adaptation of controllers based on [`LinModel`](@ref) at runtime. Modification
-of [`NonLinModel`](@ref) is not supported. New weight matrices in the objective function can
-be specified with the keyword arguments (see [`LinMPC`](@ref) for the nomenclature). If 
-`Cwt ≠ Inf`, the augmented move suppression weight is ``\mathbf{Ñ}_{H_c} = \mathrm{diag}(
-\mathbf{N}_{H_c}, C)``, else ``\mathbf{Ñ}_{H_c} = \mathbf{N}_{H_c}``. The [`StateEstimator`](@ref)
-`mpc.estim` cannot be a [`Luenberger`](@ref) observer or a [`SteadyKalmanFilter`](@ref) (the
-default estimator). Construct the `mpc` object with a time-varying [`KalmanFilter`](@ref)
-instead. Note that the model is constant over the prediction horizon ``H_p``.
+of [`NonLinModel`](@ref) state-space functions is not supported. New weight matrices in the
+objective function can be specified with the keyword arguments (see [`LinMPC`](@ref) for the
+nomenclature). If `Cwt ≠ Inf`, the augmented move suppression weight is ``\mathbf{Ñ}_{H_c} =
+\mathrm{diag}(\mathbf{N}_{H_c}, C)``, else ``\mathbf{Ñ}_{H_c} = \mathbf{N}_{H_c}``. The
+[`StateEstimator`](@ref) `mpc.estim` cannot be a [`Luenberger`](@ref) observer or a
+[`SteadyKalmanFilter`](@ref) (the default estimator). Construct the `mpc` object with a
+time-varying [`KalmanFilter`](@ref) instead. Note that the model is constant over the
+prediction horizon ``H_p``.
 
 # Arguments
 

src/estimator/execute.jl

@@ -245,13 +245,13 @@ end
 Set `model` and covariance matrices of `estim` [`StateEstimator`](@ref).
 
 Allows model adaptation of estimators based on [`LinModel`](@ref) at runtime. Modification 
-of [`NonLinModel`](@ref) is not supported. New covariance matrices can be specified with the
-keyword arguments (see [`SteadyKalmanFilter`](@ref) documentation for the nomenclature). Not 
-supported by [`Luenberger`](@ref) and [`SteadyKalmanFilter`](@ref), use the time-varying
-[`KalmanFilter`](@ref) instead.  The [`MovingHorizonEstimator`](@ref) model is kept constant
-over the estimation horizon ``H_e``. The matrix dimensions and sample time must stay the
-same. Note that the observability and controllability of the new augmented model is not 
-verified (see Extended Help for more info).
+of [`NonLinModel`](@ref) state-space functions is not supported. New covariance matrices can
+be specified with the keyword arguments (see [`SteadyKalmanFilter`](@ref) documentation for
+the nomenclature). Not supported by [`Luenberger`](@ref) and [`SteadyKalmanFilter`](@ref), 
+use the time-varying [`KalmanFilter`](@ref) instead. The [`MovingHorizonEstimator`](@ref)
+model is kept constant over the estimation horizon ``H_e``. The matrix dimensions and sample
+time must stay the same. Note that the observability and controllability of the new
+augmented model is not verified (see Extended Help for more info).
 
 # Arguments
 

test/test_predictive_control.jl

@@ -11,7 +11,7 @@ sys = [ tf(1.90,[1800.0,1])   tf(1.90,[1800.0,1])   tf(1.90,[1800.0,1]);
     @test size(mpc2.Ẽ,2) == 4*mpc2.estim.model.nu
     mpc3 = LinMPC(model, Hc=4, Cwt=1e6)
     @test size(mpc3.Ẽ,2) == 4*mpc3.estim.model.nu + 1
-    @test mpc3.C ≈ 1e6
+    @test mpc3.Ñ_Hc[end] ≈ 1e6
     mpc4 = LinMPC(model, Mwt=[1,2], Hp=15)
     @test mpc4.M_Hp ≈ Diagonal(diagm(repeat(Float64[1, 2], 15)))
     mpc5 = LinMPC(model, Nwt=[3,4], Cwt=1e3, Hc=5)
@@ -437,7 +437,7 @@ end
     @test size(nmpc2.Ẽ, 2) == 4*nonlinmodel.nu
     nmpc3 = NonLinMPC(nonlinmodel, Hp=15, Hc=4, Cwt=1e6)
     @test size(nmpc3.Ẽ, 2) == 4*nonlinmodel.nu + 1
-    @test nmpc3.C == 1e6
+    @test nmpc3.Ñ_Hc[end] == 1e6
     nmpc4 = NonLinMPC(nonlinmodel, Hp=15, Mwt=[1,2])
     @test nmpc4.M_Hp ≈ Diagonal(diagm(repeat(Float64[1, 2], 15)))
     nmpc5 = NonLinMPC(nonlinmodel, Hp=15 ,Nwt=[3,4], Cwt=1e3, Hc=5)