changed: removed noR̂u field in all PredictiveController

- it's dangerous with `setmodel!` (if `L_Hp` change, `noR̂u` can be incorrect
- the code is simpler (no branching)
- computational gains are negligible

Author: franckgaga

src/controller/construct.jl

@@ -434,9 +434,7 @@ function validate_args(mpc::PredictiveController, ry, d, D̂, R̂y, R̂u)
     size(d)  ≠ (nd,)    && throw(DimensionMismatch("d size $(size(d)) ≠ measured dist. size ($nd,)"))
     size(D̂)  ≠ (nd*Hp,) && throw(DimensionMismatch("D̂ size $(size(D̂)) ≠ measured dist. size × Hp ($(nd*Hp),)"))
     size(R̂y) ≠ (ny*Hp,) && throw(DimensionMismatch("R̂y size $(size(R̂y)) ≠ output size × Hp ($(ny*Hp),)"))
-    if ~mpc.noR̂u
-        size(R̂u) ≠ (nu*Hp,) && throw(DimensionMismatch("R̂u size $(size(R̂u)) ≠ manip. input size × Hp ($(nu*Hp),)"))
-    end
+    size(R̂u) ≠ (nu*Hp,) && throw(DimensionMismatch("R̂u size $(size(R̂u)) ≠ manip. input size × Hp ($(nu*Hp),)"))
 end
 
 

src/controller/execute.jl

@@ -204,18 +204,19 @@ function initpred!(mpc::PredictiveController, model::LinModel, d, D̂, R̂y, R̂
         mul!(F, mpc.G, mpc.d0, 1, 1)
         mul!(F, mpc.J, mpc.D̂0, 1, 1)
     end
+    # --- output setpoint tracking term ---
     mpc.R̂y .= R̂y
     Cy = F .- (R̂y .- mpc.Yop)
     M_Hp_Ẽ = mpc.weights.M_Hp*mpc.Ẽ
     mul!(q̃, M_Hp_Ẽ', Cy)
     r .= dot(Cy, mpc.weights.M_Hp, Cy)
-    if ~mpc.noR̂u
-        mpc.R̂u .= R̂u
-        Cu = mpc.T_lastu0 .- (R̂u .- mpc.Uop) 
-        L_Hp_S̃ = mpc.weights.L_Hp*mpc.S̃
-        mul!(q̃, L_Hp_S̃', Cu, 1, 1)
-        r .+= dot(Cu, mpc.weights.L_Hp, Cu)
-    end
+    # --- input setpoint tracking term ---
+    mpc.R̂u .= R̂u
+    Cu = mpc.T_lastu0 .- (R̂u .- mpc.Uop) 
+    L_Hp_S̃ = mpc.weights.L_Hp*mpc.S̃
+    mul!(q̃, L_Hp_S̃', Cu, 1, 1)
+    r .+= dot(Cu, mpc.weights.L_Hp, Cu)
+    # --- finalize ---
     lmul!(2, q̃)
     return nothing
 end
@@ -236,9 +237,7 @@ function initpred!(mpc::PredictiveController, model::SimModel, d, D̂, R̂y, R̂
         mpc.D̂e[model.nd+1:end] .= D̂
     end
     mpc.R̂y .= R̂y
-    if ~mpc.noR̂u
-        mpc.R̂u .= R̂u 
-    end
+    mpc.R̂u .= R̂u
     return nothing
 end
 
@@ -418,13 +417,9 @@ function obj_nonlinprog!(
     # --- move suppression and slack variable term ---
     JΔŨ = dot(ΔŨ, mpc.weights.Ñ_Hc, ΔŨ)
     # --- input setpoint tracking term ---
-    if !mpc.noR̂u
-        Ū  .= @views Ue[1:end-nu]
-        Ū  .= mpc.R̂u .- Ū
-        JR̂u = dot(Ū, mpc.weights.L_Hp, Ū)
-    else
-        JR̂u = 0.0
-    end
+    Ū  .= @views Ue[1:end-nu]
+    Ū  .= mpc.R̂u .- Ū
+    JR̂u = dot(Ū, mpc.weights.L_Hp, Ū)
     # --- economic term ---
     E_JE = obj_econ(mpc, model, Ue, Ŷe)
     return JR̂y + JΔŨ + JR̂u + E_JE

src/controller/explicitmpc.jl

@@ -8,7 +8,6 @@ struct ExplicitMPC{NT<:Real, SE<:StateEstimator} <: PredictiveController{NT}
     weights::ControllerWeights{NT}
     R̂u::Vector{NT}
     R̂y::Vector{NT}
-    noR̂u::Bool
     S̃::Matrix{NT} 
     T::Matrix{NT}
     T_lastu0::Vector{NT}
@@ -46,7 +45,6 @@ struct ExplicitMPC{NT<:Real, SE<:StateEstimator} <: PredictiveController{NT}
         L_Hp = Hermitian(convert(Matrix{NT}, L_Hp), :L)
         # dummy vals (updated just before optimization):
         R̂y, R̂u, T_lastu0 = zeros(NT, ny*Hp), zeros(NT, nu*Hp), zeros(NT, nu*Hp)
-        noR̂u = iszero(L_Hp)
         S, T = init_ΔUtoU(model, Hp, Hc)
         E, G, J, K, V, B = init_predmat(estim, model, Hp, Hc)
         # dummy val (updated just before optimization):
@@ -68,7 +66,7 @@ struct ExplicitMPC{NT<:Real, SE<:StateEstimator} <: PredictiveController{NT}
             ΔŨ, ŷ,
             Hp, Hc, nϵ,
             weights,
-            R̂u, R̂y, noR̂u,
+            R̂u, R̂y,
             S̃, T, T_lastu0,
             Ẽ, F, G, J, K, V, B,
             H̃, q̃, r,

src/controller/linmpc.jl

@@ -18,7 +18,6 @@ struct LinMPC{
     weights::ControllerWeights{NT}
     R̂u::Vector{NT}
     R̂y::Vector{NT}
-    noR̂u::Bool
     S̃::Matrix{NT} 
     T::Matrix{NT}
     T_lastu0::Vector{NT}
@@ -50,7 +49,6 @@ struct LinMPC{
         weights = ControllerWeights{NT}(model, Hp, Hc, M_Hp, N_Hc, L_Hp, Cwt)
         # dummy vals (updated just before optimization):
         R̂y, R̂u, T_lastu0 = zeros(NT, ny*Hp), zeros(NT, nu*Hp), zeros(NT, nu*Hp)
-        noR̂u = iszero(L_Hp)
         S, T = init_ΔUtoU(model, Hp, Hc)
         E, G, J, K, V, B, ex̂, gx̂, jx̂, kx̂, vx̂, bx̂ = init_predmat(estim, model, Hp, Hc)
         # dummy vals (updated just before optimization):
@@ -73,7 +71,7 @@ struct LinMPC{
             ΔŨ, ŷ,
             Hp, Hc, nϵ,
             weights,
-            R̂u, R̂y, noR̂u,
+            R̂u, R̂y,
             S̃, T, T_lastu0,
             Ẽ, F, G, J, K, V, B, 
             H̃, q̃, r,

src/controller/nonlinmpc.jl

@@ -23,7 +23,6 @@ struct NonLinMPC{
     p::P
     R̂u::Vector{NT}
     R̂y::Vector{NT}
-    noR̂u::Bool
     S̃::Matrix{NT}
     T::Matrix{NT}
     T_lastu0::Vector{NT}
@@ -63,7 +62,6 @@ struct NonLinMPC{
         weights = ControllerWeights{NT}(model, Hp, Hc, M_Hp, N_Hc, L_Hp, Cwt, Ewt)
         # dummy vals (updated just before optimization):
         R̂y, R̂u, T_lastu0 = zeros(NT, ny*Hp), zeros(NT, nu*Hp), zeros(NT, nu*Hp)
-        noR̂u = iszero(L_Hp)
         S, T = init_ΔUtoU(model, Hp, Hc)
         E, G, J, K, V, B, ex̂, gx̂, jx̂, kx̂, vx̂, bx̂ = init_predmat(estim, model, Hp, Hc)
         # dummy vals (updated just before optimization):
@@ -88,7 +86,7 @@ struct NonLinMPC{
             Hp, Hc, nϵ,
             weights,
             JE, p,
-            R̂u, R̂y, noR̂u,
+            R̂u, R̂y,
             S̃, T, T_lastu0,
             Ẽ, F, G, J, K, V, B,
             H̃, q̃, r,