Merge pull request #64 from JuliaControl/nonunicode_api

Offer an alternative non-unicode API for keyword arguments

docs/src/manual/nonlinmpc.md

@@ -73,10 +73,10 @@ plot(res, plotu=false)
 savefig(ans, "plot1_NonLinMPC.svg"); nothing # hide
 ```
 
-The [`setname!`](@ref) function allows customizing the Y-axis labels.
-
 ![plot1_NonLinMPC](plot1_NonLinMPC.svg)
 
+The [`setname!`](@ref) function allows customizing the Y-axis labels.
+
 ## Nonlinear Model Predictive Controller
 
 An [`UnscentedKalmanFilter`](@ref) estimates the plant state :

src/controller/construct.jl

@@ -53,6 +53,14 @@ for details on bounds and softness parameters ``\mathbf{c}``. The output and ter
 constraints are all soft by default. See Extended Help for time-varying constraints.
 
 # Arguments
+!!! info
+    The keyword arguments `Δumin`, `Δumax`, `c_Δumin`, `c_Δumax`, `x̂min`, `x̂max`, `c_x̂min`,
+    `c_x̂max` and their capital letter versions have non-Unicode alternatives e.g. 
+    *`Deltaumin`*, *`xhatmax`* and *`C_Deltaumin`*
+
+    The default constraints are mentioned here for clarity but omitting a keyword argument 
+    will not re-assign to its default value (defaults are set at construction only).
+
 - `mpc::PredictiveController` : predictive controller to set constraints
 - `umin=fill(-Inf,nu)` / `umax=fill(+Inf,nu)` : manipulated input bound ``\mathbf{u_{min/max}}``
 - `Δumin=fill(-Inf,nu)` / `Δumax=fill(+Inf,nu)` : manipulated input increment bound ``\mathbf{Δu_{min/max}}``
@@ -114,20 +122,26 @@ LinMPC controller with a sample time Ts = 4.0 s, OSQP optimizer, SteadyKalmanFil
 """
 function setconstraint!(
     mpc::PredictiveController; 
-    umin    = nothing, umax    = nothing,
-    Δumin   = nothing, Δumax   = nothing,
-    ymin    = nothing, ymax    = nothing,
-    x̂min    = nothing, x̂max    = nothing,
-    c_umin  = nothing, c_umax  = nothing,
-    c_Δumin = nothing, c_Δumax = nothing,
-    c_ymin  = nothing, c_ymax  = nothing,
-    c_x̂min  = nothing, c_x̂max  = nothing,
-    Umin    = nothing, Umax    = nothing,
-    ΔUmin   = nothing, ΔUmax   = nothing,
-    Ymin    = nothing, Ymax    = nothing,
-    C_umax  = nothing, C_umin  = nothing,
-    C_Δumax = nothing, C_Δumin = nothing,
-    C_ymax  = nothing, C_ymin  = nothing,
+    umin        = nothing, umax        = nothing,
+    Deltaumin   = nothing, Deltaumax   = nothing,
+    ymin        = nothing, ymax        = nothing,
+    xhatmin     = nothing, xhatmax     = nothing,
+    c_umin      = nothing, c_umax      = nothing,
+    c_Deltaumin = nothing, c_Deltaumax = nothing,
+    c_ymin      = nothing, c_ymax      = nothing,
+    c_xhatmin   = nothing, c_xhatmax   = nothing,
+    Umin        = nothing, Umax        = nothing,
+    DeltaUmin   = nothing, DeltaUmax   = nothing,
+    Ymin        = nothing, Ymax        = nothing,
+    C_umax      = nothing, C_umin      = nothing,
+    C_Deltaumax = nothing, C_Deltaumin = nothing,
+    C_ymax      = nothing, C_ymin      = nothing,
+    Δumin   = Deltaumin,   Δumax = Deltaumax,
+    x̂min    = xhatmin,     x̂max = xhatmax,
+    c_Δumin = c_Deltaumin, c_Δumax = c_Deltaumax,
+    c_x̂min  = c_xhatmin,   c_x̂max = c_xhatmax,
+    ΔUmin   = DeltaUmin,   ΔUmax = DeltaUmax,
+    C_Δumin = C_Deltaumin, C_Δumax = C_Deltaumax,
 )
     model, con, optim = mpc.estim.model, mpc.con, mpc.optim
     nu, ny, nx̂, Hp, Hc = model.nu, model.ny, mpc.estim.nx̂, mpc.Hp, mpc.Hc

src/controller/execute.jl

@@ -24,15 +24,19 @@ Calling a [`PredictiveController`](@ref) object calls this method.
 See also [`LinMPC`](@ref), [`ExplicitMPC`](@ref), [`NonLinMPC`](@ref).
 
 # Arguments
+
+Keyword arguments in *`italic`* are non-Unicode alternatives.
+
 - `mpc::PredictiveController` : solve optimization problem of `mpc`.
 - `ry=mpc.estim.model.yop` : current output setpoints ``\mathbf{r_y}(k)``.
 - `d=[]` : current measured disturbances ``\mathbf{d}(k)``.
-- `D̂=repeat(d, mpc.Hp)` : predicted measured disturbances ``\mathbf{D̂}``, constant in the
-  future by default or ``\mathbf{d̂}(k+j)=\mathbf{d}(k)`` for ``j=1`` to ``H_p``.
-- `R̂y=repeat(ry, mpc.Hp)` : predicted output setpoints ``\mathbf{R̂_y}``, constant in the
-  future by default or ``\mathbf{r̂_y}(k+j)=\mathbf{r_y}(k)`` for ``j=1`` to ``H_p``.
-- `R̂u=repeat(mpc.estim.model.uop, mpc.Hp)` : predicted manipulated input setpoints, constant
-  in the future by default or ``\mathbf{r̂_u}(k+j)=\mathbf{u_{op}}`` for ``j=0`` to ``H_p-1``.
+- `D̂=repeat(d, mpc.Hp)` or *`Dhat`* : predicted measured disturbances ``\mathbf{D̂}``, constant
+   in the future by default or ``\mathbf{d̂}(k+j)=\mathbf{d}(k)`` for ``j=1`` to ``H_p``.
+- `R̂y=repeat(ry, mpc.Hp)` or *`Rhaty`* : predicted output setpoints ``\mathbf{R̂_y}``, constant
+   in the future by default or ``\mathbf{r̂_y}(k+j)=\mathbf{r_y}(k)`` for ``j=1`` to ``H_p``.
+- `R̂u=repeat(mpc.estim.model.uop, mpc.Hp)` or *`Rhatu`* : predicted manipulated input
+   setpoints, constant in the future by default or ``\mathbf{r̂_u}(k+j)=\mathbf{u_{op}}`` for
+   ``j=0`` to ``H_p-1``. 
 - `ym=nothing` : current measured outputs ``\mathbf{y^m}(k)``, only required if `mpc.estim` 
    is an [`InternalModel`](@ref).
 
@@ -49,10 +53,13 @@ function moveinput!(
     mpc::PredictiveController, 
     ry::Vector = mpc.estim.model.yop, 
     d ::Vector = empty(mpc.estim.x̂0);
-    D̂ ::Vector = repeat(d,  mpc.Hp),
-    R̂y::Vector = repeat(ry, mpc.Hp),
-    R̂u::Vector = mpc.noR̂u ? empty(mpc.estim.x̂0) : repeat(mpc.estim.model.uop, mpc.Hp),
-    ym::Union{Vector, Nothing} = nothing
+    Dhat ::Vector = repeat(d,  mpc.Hp),
+    Rhaty::Vector = repeat(ry, mpc.Hp),
+    Rhatu::Vector = mpc.noR̂u ? empty(mpc.estim.x̂0) : repeat(mpc.estim.model.uop, mpc.Hp),
+    ym::Union{Vector, Nothing} = nothing,
+    D̂  = Dhat,
+    R̂y = Rhaty,
+    R̂u = Rhatu
 )
     validate_args(mpc, ry, d, D̂, R̂y, R̂u)
     initpred!(mpc, mpc.estim.model, d, ym, D̂, R̂y, R̂u)
@@ -73,19 +80,22 @@ Get additional info about `mpc` [`PredictiveController`](@ref) optimum for troub
 The function should be called after calling [`moveinput!`](@ref). It returns the dictionary
 `info` with the following fields:
 
-- `:ΔU`  : optimal manipulated input increments over ``H_c``, ``\mathbf{ΔU}``
-- `:ϵ`   : optimal slack variable, ``ϵ``
+!!! info
+    Fields in *`italic`* are non-Unicode alternatives.
+
+- `:ΔU` or *`:DeltaU`* : optimal manipulated input increments over ``H_c``, ``\mathbf{ΔU}``
+- `:ϵ` or *`:epsilon`* : optimal slack variable, ``ϵ``
+- `:D̂` or *`:Dhat`* : predicted measured disturbances over ``H_p``, ``\mathbf{D̂}``
+- `:ŷ` or *`:yhat`* : current estimated output, ``\mathbf{ŷ}(k)``
+- `:Ŷ` or *`:Yhat`* : optimal predicted outputs over ``H_p``, ``\mathbf{Ŷ}``
+- `:Ŷs` or *`:Yhats`* : predicted stochastic output over ``H_p`` of [`InternalModel`](@ref), ``\mathbf{Ŷ_s}``
+- `:R̂y` or *`:Rhaty`* : predicted output setpoint over ``H_p``, ``\mathbf{R̂_y}``
+- `:R̂u` or *`:Rhatu`* : predicted manipulated input setpoint over ``H_p``, ``\mathbf{R̂_u}``
+- `:x̂end` or *`:xhatend`* : optimal terminal states, ``\mathbf{x̂}_{k-1}(k+H_p)``
 - `:J`   : objective value optimum, ``J``
 - `:U`   : optimal manipulated inputs over ``H_p``, ``\mathbf{U}``
 - `:u`   : current optimal manipulated input, ``\mathbf{u}(k)``
 - `:d`   : current measured disturbance, ``\mathbf{d}(k)``
-- `:D̂`   : predicted measured disturbances over ``H_p``, ``\mathbf{D̂}``
-- `:ŷ`   : current estimated output, ``\mathbf{ŷ}(k)``
-- `:Ŷ`   : optimal predicted outputs over ``H_p``, ``\mathbf{Ŷ}``
-- `:x̂end`: optimal terminal states, ``\mathbf{x̂}_{k-1}(k+H_p)``
-- `:Ŷs`  : predicted stochastic output over ``H_p`` of [`InternalModel`](@ref), ``\mathbf{Ŷ_s}``
-- `:R̂y`  : predicted output setpoint over ``H_p``, ``\mathbf{R̂_y}``
-- `:R̂u`  : predicted manipulated input setpoint over ``H_p``, ``\mathbf{R̂_u}``
 
 For [`LinMPC`](@ref) and [`NonLinMPC`](@ref), the field `:sol` also contains the optimizer
 solution summary that can be printed. Lastly, the optimal economic cost `:JE` is also
@@ -129,6 +139,16 @@ function getinfo(mpc::PredictiveController{NT}) where NT<:Real
     info[:Ŷs]   = Ŷs
     info[:R̂y]   = mpc.R̂y0 + mpc.Yop
     info[:R̂u]   = mpc.R̂u0 + mpc.Uop
+    # --- non-Unicode fields ---
+    info[:DeltaU] = info[:ΔU]
+    info[:epsilon] = info[:ϵ]
+    info[:Dhat] = info[:D̂]
+    info[:yhat] = info[:ŷ]
+    info[:Yhat] = info[:Ŷ]
+    info[:xhatend] = info[:x̂end]
+    info[:Yhats] = info[:Ŷs]
+    info[:Rhaty] = info[:R̂y]
+    info[:Rhatu] = info[:R̂u]
     info = addinfo!(info, mpc)
     return info
 end
@@ -498,7 +518,7 @@ updatestate!(::PredictiveController, _ ) = throw(ArgumentError("missing measured
 """
     setstate!(mpc::PredictiveController, x̂) -> mpc
 
-Set the estimate at `mpc.estim.x̂`.
+Set `mpc.estim.x̂0` to `x̂ - estim.x̂op` from the argument `x̂`.
 """
 setstate!(mpc::PredictiveController, x̂) = (setstate!(mpc.estim, x̂); return mpc)
 

src/estimator/kalman.jl

@@ -86,21 +86,24 @@ the rows of ``\mathbf{Ĉ, D̂_d}`` that correspond to measured outputs ``\mathb
 unmeasured ones, for ``\mathbf{Ĉ^u, D̂_d^u}``).
 
 # Arguments
+!!! info
+    Keyword arguments in *`italic`* are non-Unicode alternatives.
+
 - `model::LinModel` : (deterministic) model for the estimations.
 - `i_ym=1:model.ny` : `model` output indices that are measured ``\mathbf{y^m}``, the rest 
     are unmeasured ``\mathbf{y^u}``.
-- `σQ=fill(1/model.nx,model.nx)` : main diagonal of the process noise covariance
-    ``\mathbf{Q}`` of `model`, specified as a standard deviation vector.
-- `σR=fill(1,length(i_ym))` : main diagonal of the sensor noise covariance ``\mathbf{R}``
-    of `model` measured outputs, specified as a standard deviation vector.
+- `σQ=fill(1/model.nx,model.nx)` or *`sigmaQ`* : main diagonal of the process noise
+    covariance ``\mathbf{Q}`` of `model`, specified as a standard deviation vector.
+- `σR=fill(1,length(i_ym))` or *`sigmaR`* : main diagonal of the sensor noise covariance
+    ``\mathbf{R}`` of `model` measured outputs, specified as a standard deviation vector.
 - `nint_u=0`: integrator quantity for the stochastic model of the unmeasured disturbances at
     the manipulated inputs (vector), use `nint_u=0` for no integrator (see Extended Help).
-- `σQint_u=fill(1,sum(nint_u))`: same than `σQ` but for the unmeasured disturbances at 
-    manipulated inputs ``\mathbf{Q_{int_u}}`` (composed of integrators).
 - `nint_ym=default_nint(model,i_ym,nint_u)` : same than `nint_u` but for the unmeasured 
     disturbances at the measured outputs, use `nint_ym=0` for no integrator (see Extended Help).
-- `σQint_ym=fill(1,sum(nint_ym))` : same than `σQ` for the unmeasured disturbances at 
-    measured outputs ``\mathbf{Q_{int_{ym}}}`` (composed of integrators).
+- `σQint_u=fill(1,sum(nint_u))` or *`sigmaQint_u`* : same than `σQ` but for the unmeasured
+    disturbances at manipulated inputs ``\mathbf{Q_{int_u}}`` (composed of integrators).
+- `σQint_ym=fill(1,sum(nint_ym))` or *`sigmaQint_u`* : same than `σQ` for the unmeasured
+    disturbances at measured outputs ``\mathbf{Q_{int_{ym}}}`` (composed of integrators).
 
 # Examples
 ```jldoctest
@@ -139,12 +142,16 @@ SteadyKalmanFilter estimator with a sample time Ts = 0.5 s, LinModel and:
 function SteadyKalmanFilter(
     model::SM;
     i_ym::IntRangeOrVector = 1:model.ny,
-    σQ::Vector = fill(1/model.nx, model.nx),
-    σR::Vector = fill(1, length(i_ym)),
+    sigmaQ = fill(1/model.nx, model.nx),
+    sigmaR = fill(1, length(i_ym)),
     nint_u ::IntVectorOrInt = 0,
-    σQint_u::Vector = fill(1, max(sum(nint_u), 0)),
     nint_ym::IntVectorOrInt = default_nint(model, i_ym, nint_u),
-    σQint_ym::Vector = fill(1, max(sum(nint_ym), 0))
+    sigmaQint_u  = fill(1, max(sum(nint_u),  0)),
+    sigmaQint_ym = fill(1, max(sum(nint_ym), 0)),
+    σQ       = sigmaQ,
+    σR       = sigmaR,
+    σQint_u  = sigmaQint_u,
+    σQint_ym = sigmaQint_ym,
 ) where {NT<:Real, SM<:LinModel{NT}}
     # estimated covariances matrices (variance = σ²) :
     Q̂  = Hermitian(diagm(NT[σQ;  σQint_u;  σQint_ym ].^2), :L)
@@ -266,13 +273,16 @@ its initial value with ``\mathbf{P̂}_{-1}(0) =
     \mathrm{diag}\{ \mathbf{P}(0), \mathbf{P_{int_{u}}}(0), \mathbf{P_{int_{ym}}}(0) \}``.
 
 # Arguments
+!!! info
+    Keyword arguments in *`italic`* are non-Unicode alternatives.
+
 - `model::LinModel` : (deterministic) model for the estimations.
-- `σP_0=fill(1/model.nx,model.nx)` : main diagonal of the initial estimate covariance
-    ``\mathbf{P}(0)``, specified as a standard deviation vector.
-- `σPint_u_0=fill(1,sum(nint_u))` : same than `σP_0` but for the unmeasured disturbances at 
-    manipulated inputs ``\mathbf{P_{int_u}}(0)`` (composed of integrators).
-- `σPint_ym_0=fill(1,sum(nint_ym))` : same than `σP_0` but for the unmeasured disturbances
-    at measured outputs ``\mathbf{P_{int_{ym}}}(0)`` (composed of integrators).
+- `σP_0=fill(1/model.nx,model.nx)` or *`sigmaP_0`* : main diagonal of the initial estimate
+    covariance ``\mathbf{P}(0)``, specified as a standard deviation vector.
+- `σPint_u_0=fill(1,sum(nint_u))` or *`sigmaPint_u_0`* : same than `σP_0` but for the unmeasured
+    disturbances at manipulated inputs ``\mathbf{P_{int_u}}(0)`` (composed of integrators).
+- `σPint_ym_0=fill(1,sum(nint_ym))` or *`sigmaPint_ym_0`* : same than `σP_0` but for the unmeasured
+    disturbances at measured outputs ``\mathbf{P_{int_{ym}}}(0)`` (composed of integrators).
 - `<keyword arguments>` of [`SteadyKalmanFilter`](@ref) constructor.
 
 # Examples
@@ -291,15 +301,22 @@ KalmanFilter estimator with a sample time Ts = 0.5 s, LinModel and:
 function KalmanFilter(
     model::SM;
     i_ym::IntRangeOrVector = 1:model.ny,
-    σP_0::Vector = fill(1/model.nx, model.nx),
-    σQ ::Vector = fill(1/model.nx, model.nx),
-    σR ::Vector = fill(1, length(i_ym)),
-    nint_u   ::IntVectorOrInt = 0,
-    σQint_u  ::Vector = fill(1, max(sum(nint_u), 0)),
-    σPint_u_0 ::Vector = fill(1, max(sum(nint_u), 0)),
-    nint_ym  ::IntVectorOrInt = default_nint(model, i_ym, nint_u),
-    σQint_ym ::Vector = fill(1, max(sum(nint_ym), 0)),
-    σPint_ym_0::Vector = fill(1, max(sum(nint_ym), 0)),
+    sigmaP_0 = fill(1/model.nx, model.nx),
+    sigmaQ   = fill(1/model.nx, model.nx),
+    sigmaR   = fill(1, length(i_ym)),
+    nint_u ::IntVectorOrInt = 0,
+    nint_ym::IntVectorOrInt = default_nint(model, i_ym, nint_u),
+    sigmaPint_u_0  = fill(1, max(sum(nint_u),  0)),
+    sigmaQint_u    = fill(1, max(sum(nint_u),  0)),
+    sigmaPint_ym_0 = fill(1, max(sum(nint_ym), 0)),
+    sigmaQint_ym   = fill(1, max(sum(nint_ym), 0)),
+    σP_0       = sigmaP_0,
+    σQ         = sigmaQ,
+    σR         = sigmaR,
+    σPint_u_0  = sigmaPint_u_0,
+    σQint_u    = sigmaQint_u,
+    σPint_ym_0 = sigmaPint_ym_0,
+    σQint_ym   = sigmaQint_ym,
 ) where {NT<:Real, SM<:LinModel{NT}}
     # estimated covariances matrices (variance = σ²) :
     P̂_0 = Hermitian(diagm(NT[σP_0; σPint_u_0; σPint_ym_0].^2), :L)
@@ -440,10 +457,13 @@ represents the measured outputs of ``\mathbf{ĥ}`` function (and unmeasured one
 ``\mathbf{ĥ^u}``).
 
 # Arguments
+!!! info
+    Keyword arguments in *`italic`* are non-Unicode alternatives.
+
 - `model::SimModel` : (deterministic) model for the estimations.
-- `α=1e-3` : alpha parameter, spread of the state distribution ``(0 < α ≤ 1)``.
-- `β=2` : beta parameter, skewness and kurtosis of the states distribution ``(β ≥ 0)``.
-- `κ=0` : kappa parameter, another spread parameter ``(0 ≤ κ ≤ 3)``.
+- `α=1e-3` or *`alpha`* : alpha parameter, spread of the state distribution ``(0 < α ≤ 1)``.
+- `β=2` or *`beta`* : beta parameter, skewness and kurtosis of the states distribution ``(β ≥ 0)``.
+- `κ=0` or *`kappa`* : kappa parameter, another spread parameter ``(0 ≤ κ ≤ 3)``.
 - `<keyword arguments>` of [`SteadyKalmanFilter`](@ref) constructor.
 - `<keyword arguments>` of [`KalmanFilter`](@ref) constructor.
 
@@ -470,18 +490,28 @@ UnscentedKalmanFilter estimator with a sample time Ts = 10.0 s, NonLinModel and:
 function UnscentedKalmanFilter(
     model::SM;
     i_ym::IntRangeOrVector = 1:model.ny,
-    σP_0::Vector = fill(1/model.nx, model.nx),
-    σQ ::Vector = fill(1/model.nx, model.nx),
-    σR ::Vector = fill(1, length(i_ym)),
-    nint_u   ::IntVectorOrInt = 0,
-    σQint_u  ::Vector = fill(1, max(sum(nint_u), 0)),
-    σPint_u_0 ::Vector = fill(1, max(sum(nint_u), 0)),
-    nint_ym  ::IntVectorOrInt = default_nint(model, i_ym, nint_u),
-    σQint_ym ::Vector = fill(1, max(sum(nint_ym), 0)),
-    σPint_ym_0::Vector = fill(1, max(sum(nint_ym), 0)),
-    α::Real = 1e-3,
-    β::Real = 2,
-    κ::Real = 0
+    sigmaP_0 = fill(1/model.nx, model.nx),
+    sigmaQ   = fill(1/model.nx, model.nx),
+    sigmaR   = fill(1, length(i_ym)),
+    nint_u ::IntVectorOrInt = 0,
+    nint_ym::IntVectorOrInt = default_nint(model, i_ym, nint_u),
+    sigmaPint_u_0  = fill(1, max(sum(nint_u),  0)),
+    sigmaQint_u    = fill(1, max(sum(nint_u),  0)),
+    sigmaPint_ym_0 = fill(1, max(sum(nint_ym), 0)),
+    sigmaQint_ym   = fill(1, max(sum(nint_ym), 0)),
+    alpha::Real = 1e-3,
+    beta ::Real = 2,
+    kappa::Real = 0,
+    σP_0       = sigmaP_0,
+    σQ         = sigmaQ,
+    σR         = sigmaR,
+    σPint_u_0  = sigmaPint_u_0,
+    σQint_u    = sigmaQint_u,
+    σPint_ym_0 = sigmaPint_ym_0,
+    σQint_ym   = sigmaQint_ym,
+    α = alpha,
+    β = beta,
+    κ = kappa,
 ) where {NT<:Real, SM<:SimModel{NT}}
     # estimated covariances matrices (variance = σ²) :
     P̂_0 = Hermitian(diagm(NT[σP_0; σPint_u_0; σPint_ym_0].^2), :L)
@@ -732,15 +762,22 @@ ExtendedKalmanFilter estimator with a sample time Ts = 5.0 s, NonLinModel and:
 function ExtendedKalmanFilter(
     model::SM;
     i_ym::IntRangeOrVector = 1:model.ny,
-    σP_0::Vector = fill(1/model.nx, model.nx),
-    σQ ::Vector = fill(1/model.nx, model.nx),
-    σR ::Vector = fill(1, length(i_ym)),
-    nint_u   ::IntVectorOrInt = 0,
-    σQint_u  ::Vector = fill(1, max(sum(nint_u), 0)),
-    σPint_u_0 ::Vector = fill(1, max(sum(nint_u), 0)),
-    nint_ym  ::IntVectorOrInt = default_nint(model, i_ym, nint_u),
-    σQint_ym ::Vector = fill(1, max(sum(nint_ym), 0)),
-    σPint_ym_0::Vector = fill(1, max(sum(nint_ym), 0)),
+    sigmaP_0 = fill(1/model.nx, model.nx),
+    sigmaQ   = fill(1/model.nx, model.nx),
+    sigmaR   = fill(1, length(i_ym)),
+    nint_u ::IntVectorOrInt = 0,
+    nint_ym::IntVectorOrInt = default_nint(model, i_ym, nint_u),
+    sigmaPint_u_0  = fill(1, max(sum(nint_u),  0)),
+    sigmaQint_u    = fill(1, max(sum(nint_u),  0)),
+    sigmaPint_ym_0 = fill(1, max(sum(nint_ym), 0)),
+    sigmaQint_ym   = fill(1, max(sum(nint_ym), 0)),
+    σP_0       = sigmaP_0,
+    σQ         = sigmaQ,
+    σR         = sigmaR,
+    σPint_u_0  = sigmaPint_u_0,
+    σQint_u    = sigmaQint_u,
+    σPint_ym_0 = sigmaPint_ym_0,
+    σQint_ym   = sigmaQint_ym,
 ) where {NT<:Real, SM<:SimModel{NT}}
     # estimated covariances matrices (variance = σ²) :
     P̂_0 = Hermitian(diagm(NT[σP_0; σPint_u_0; σPint_ym_0].^2), :L)