debug: correct signature

Author: franckgaga

src/controller/execute.jl

@@ -10,7 +10,7 @@ It also stores `u - mpc.estim.model.uop` at `mpc.lastu0` for converting the inpu
 function initstate!(mpc::PredictiveController, u, ym, d=mpc.estim.buffer.empty)
     x̂ = initstate!(mpc.estim, u, ym, d)
     mpc.lastu0 .= u .- mpc.estim.model.uop
-    init_decision!(mpc, mpc.transcription, x̂)
+    init_decision!(mpc, mpc.transcription)
     return x̂
 end
 

src/controller/transcription.jl

@@ -771,9 +771,9 @@ end
 
 Init the decision vector ``\mathbf{Z̃ = ΔŨ}`` with zeros for [`SingleShooting`](@ref).
 """
-init_decision!(mpc::PredictiveController, ::SingleShooting, _) = (mpc.Z̃ .= 0; nothing)
+init_decision!(mpc::PredictiveController, ::SingleShooting) = (mpc.Z̃ .= 0; nothing)
 
-@dow raw"""
+@doc raw"""
     init_decision!(mpc::PredictiveController, ::TranscriptionMethod)
 
 Also init the state part of the decision vector for other [`TranscriptionMethod`](@ref)s.
@@ -782,7 +782,7 @@ The ``\mathbf{X̂_0}`` component is assumed constant over ``H_p`` at the current
 in `mpc.estim.x̂0`.
 """
 function init_decision!(mpc::PredictiveController, ::TranscriptionMethod)
-    nΔU, nx̂ = mpc.Hc*mpc.model.nu, mpc.estim.nx̂
+    nΔU, nx̂ = mpc.Hc*mpc.estim.model.nu, mpc.estim.nx̂
     mpc.Z̃ .= 0
     for j=1:mpc.Hp
         iRow = nΔU .+ (1:nx̂) .+ nx̂*(j-1)

src/plot_sim.jl

@@ -286,7 +286,11 @@ function sim_closedloop!(
     X̂_data    = Matrix{NT}(undef, estim.nx̂, N)
     setstate!(plant, x_0)
     lastd, lasty = d, evaloutput(plant, d)
+    # 1st `setstate!`, for correct `mpc.Z̃` initialization in `initstate!`:
+    isnothing(x̂_0) || setstate!(est_mpc, x̂_0) 
+    # calling `initstate!` no matter what, to initialize `mpc.Z̃` and covariance matrices:
     initstate!(est_mpc, lastu, lasty[estim.i_ym], lastd)
+    # 2nd `setstate!`, since `initstate!` overwrite the state for `LinModel`:
     isnothing(x̂_0) || setstate!(est_mpc, x̂_0)
     @progressif progress name="$(nameof(typeof(est_mpc))) simulation" for i=1:N
         d = lastd + d_step + d_noise.*randn(plant.nd)