Removed some Union{} in fields

Author: franckgaga

example/juMPC.jl

@@ -129,7 +129,7 @@ function test_mpc(model, mpc)
     return u_data, y_data, r_data, d_data
 end
 
-u_data, y_data, r_data, d_data = test_mpc(linModel4, mpc)
+@time u_data, y_data, r_data, d_data = test_mpc(linModel4, mpc)
 #profview u_data, y_data, r_data, d_data = test_mpc(linModel4, mpc)
 #=
 using PlotThemes, Plots

src/estimator/internal_model.jl

@@ -3,7 +3,7 @@ struct InternalModel{M<:SimModel} <: StateEstimator
     x̂::Vector{Float64}
     x̂d::Vector{Float64}
     x̂s::Vector{Float64}
-    i_ym::IntRangeOrVector
+    i_ym::Vector{Int}
     nx̂::Int
     nym::Int
     nyu::Int
@@ -36,6 +36,7 @@ struct InternalModel{M<:SimModel} <: StateEstimator
         nx̂ = model.nx
         nxs = size(As,1)
         Âs, B̂s = init_internalmodel(As, Bs, Cs, Ds)
+        i_ym = collect(i_ym)
         x̂d = x̂ = copy(model.x) # x̂ and x̂d are same object (updating x̂d will update x̂)
         x̂s = zeros(nxs)
         return new(model, x̂, x̂d, x̂s, i_ym, nx̂, nym, nyu, nxs, As, Bs, Cs, Ds, Âs, B̂s)

src/estimator/kalman.jl

@@ -1,7 +1,7 @@
 struct SteadyKalmanFilter <: StateEstimator
     model::LinModel
     x̂::Vector{Float64}
-    i_ym::IntRangeOrVector
+    i_ym::Vector{Int}
     nx̂::Int
     nym::Int
     nyu::Int
@@ -39,6 +39,7 @@ struct SteadyKalmanFilter <: StateEstimator
                       "parameter or use the time-varying KalmanFilter.")
             end
         end
+        i_ym = collect(i_ym)
         Q̂ = Hermitian(Q̂, :L)
         R̂ = Hermitian(R̂, :L)
         x̂ = [copy(model.x); zeros(nxs)]
@@ -172,7 +173,7 @@ struct KalmanFilter <: StateEstimator
     model::LinModel
     x̂::Vector{Float64}
     P̂::Hermitian{Float64, Matrix{Float64}}
-    i_ym::IntRangeOrVector
+    i_ym::Vector{Int}
     nx̂::Int
     nym::Int
     nyu::Int
@@ -201,6 +202,7 @@ struct KalmanFilter <: StateEstimator
         As, _ , Cs, _  = stoch_ym2y(model, i_ym, Asm, [], Csm, [])
         f̂, ĥ, Â, B̂u, Ĉ, B̂d, D̂d = augment_model(model, As, Cs)
         Ĉm, D̂dm = Ĉ[i_ym, :], D̂d[i_ym, :] # measured outputs ym only
+        i_ym = collect(i_ym)
         x̂ = [copy(model.x); zeros(nxs)]
         P̂0 = Hermitian(P̂0, :L)
         Q̂ = Hermitian(Q̂, :L)
@@ -324,7 +326,7 @@ struct UnscentedKalmanFilter{M<:SimModel} <: StateEstimator
     model::M
     x̂::Vector{Float64}
     P̂::Hermitian{Float64, Matrix{Float64}}
-    i_ym::IntRangeOrVector
+    i_ym::Vector{Int}
     nx̂::Int
     nym::Int
     nyu::Int
@@ -352,6 +354,7 @@ struct UnscentedKalmanFilter{M<:SimModel} <: StateEstimator
         As, _ , Cs, _  = stoch_ym2y(model, i_ym, Asm, [], Csm, [])
         f̂, ĥ = augment_model(model, As, Cs)
         nσ, γ, m̂, Ŝ = init_ukf(nx̂, α, β, κ)
+        i_ym = collect(i_ym)
         x̂ = [copy(model.x); zeros(nxs)]
         P̂0 = Hermitian(P̂0, :L)
         Q̂ = Hermitian(Q̂, :L)

src/estimator/luenberger.jl

@@ -1,7 +1,7 @@
 struct Luenberger <: StateEstimator
     model::LinModel
     x̂::Vector{Float64}
-    i_ym::IntRangeOrVector
+    i_ym::Vector{Int}
     nx̂::Int
     nym::Int
     nyu::Int
@@ -29,6 +29,7 @@ struct Luenberger <: StateEstimator
         f̂, ĥ, Â, B̂u, Ĉ, B̂d, D̂d = augment_model(model, As, Cs)
         Ĉm, D̂dm = Ĉ[i_ym, :], D̂d[i_ym, :] # measured outputs ym only
         K = L
+        i_ym = collect(i_ym)
         x̂ = [copy(model.x); zeros(nxs)]
         return new(
             model, 

src/predictive_control.jl

@@ -21,7 +21,7 @@ abstract type PredictiveController end
 
 mutable struct OptimInfo
     ΔŨ::Vector{Float64}
-    ϵ ::Union{Nothing, Float64}
+    ϵ ::Float64
     J ::Float64
     u ::Vector{Float64}
     U ::Vector{Float64}
@@ -117,7 +117,7 @@ struct LinMPC{S<:StateEstimator} <: PredictiveController
         b = b[i_nonInf]
         @constraint(optim, constraint_lin, A*ΔŨ .≤ b)
         ΔŨ0 = zeros(nvar)
-        ϵ = isinf(C) ? nothing : 0.0 # C = Inf means hard constraints only
+        ϵ = isinf(C) ? NaN : 0.0 # C = Inf means hard constraints only
         u, U = copy(model.uop), repeat(model.uop, Hp)
         ŷ, Ŷ = copy(model.yop), repeat(model.yop, Hp)
         ŷs, Ŷs = zeros(ny), zeros(ny*Hp)
@@ -347,7 +347,7 @@ struct NonLinMPC{S<:StateEstimator} <: PredictiveController
         #b = b[i_nonInf]
         #@constraint(optim, constraint_lin, A*ΔŨ .≤ b)
         ΔŨ0 = zeros(nvar)
-        ϵ = isinf(C) ? nothing : 0.0 # C = Inf means hard constraints only
+        ϵ = isinf(C) ? NaN : 0.0 # C = Inf means hard constraints only
         u, U = copy(model.uop), repeat(model.uop, Hp)
         ŷ, Ŷ = copy(model.yop), repeat(model.yop, Hp)
         ŷs, Ŷs = zeros(ny), zeros(ny*Hp)
@@ -841,7 +841,7 @@ Write `mpc.info` with the [`LinMPC`](@ref) optimization results.
 """
 function write_info!(mpc::LinMPC, ΔŨ, J, ŷs, Ŷs, lastu, F, ym, d)
     mpc.info.ΔŨ = ΔŨ
-    mpc.info.ϵ = isinf(mpc.C) ? nothing : ΔŨ[end]
+    mpc.info.ϵ = isinf(mpc.C) ? NaN : ΔŨ[end]
     mpc.info.J = J
     mpc.info.U = mpc.S̃_Hp*ΔŨ + mpc.T_Hp*lastu
     mpc.info.u = mpc.info.U[1:mpc.estim.model.nu]