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Merged
franckgaga merged 1 commit into from
Sep 4, 2026
Merged

changed: clearer notation for intermadiate stage vector (k -> k̄) #440

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2 changes: 1 addition & 1 deletion src/controller/execute.jl
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Expand Up @@ -144,7 +144,7 @@ julia> round.(getinfo(mpc)[:Ŷ], digits=3)
"""
function getinfo(mpc::PredictiveController{NT}) where NT<:Real
model, buffer, transcription = mpc.estim.model, mpc.buffer, mpc.transcription
nΔŨ, nK = mpc.Hc*model.nu + mpc.nϵ, mpc.Hp*model.nk
nΔŨ, nK = mpc.Hc*model.nu + mpc.nϵ, mpc.Hp*model.nk̄
nŶe, nUe = (mpc.Hp+1)*model.ny, (mpc.Hp+1)*model.nu
nX̂0, nÛ0 = mpc.estim.nx̂*mpc.Hp, model.nu*mpc.Hp
Z̃ = mpc.Z̃
Expand Down
12 changes: 6 additions & 6 deletions src/controller/nonlinmpc.jl
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Expand Up @@ -584,12 +584,12 @@ function addinfo!(info, mpc::NonLinMPC{NT}) where NT<:Real
hess = mpc.hessian
transcription = mpc.transcription
nu, ny, nx̂, nϵ = model.nu, model.ny, mpc.estim.nx̂, mpc.nϵ
nk = get_nk(model, transcription)
nk̄ = get_nk̄(model, transcription)
Hp, Hc = mpc.Hp, mpc.Hc
i_g = findall(mpc.con.i_g) # convert to non-logical indices for non-allocating @views
ng, ngi = length(mpc.con.i_g), sum(mpc.con.i_g)
nc, neq = con.nc, con.neq
nU, nŶ, nX̂, nK = mpc.Hp*nu, Hp*ny, Hp*nx̂, Hp*nk
nU, nŶ, nX̂, nK = mpc.Hp*nu, Hp*ny, Hp*nx̂, Hp*nk̄
nΔŨ, nUe, nŶe = nu*Hc + nϵ, nU + nu, nŶ + ny
ΔŨ = zeros(NT, nΔŨ)
x̂0end = zeros(NT, nx̂)
Expand Down Expand Up @@ -823,11 +823,11 @@ function get_nonlinobj_op(mpc::NonLinMPC, optim::JuMP.GenericModel{JNT}) where J
transcription = mpc.transcription
grad, hess = mpc.gradient, mpc.hessian
nu, ny, nx̂, nϵ = model.nu, model.ny, mpc.estim.nx̂, mpc.nϵ
nk = get_nk(model, transcription)
nk̄ = get_nk̄(model, transcription)
Hp, Hc = mpc.Hp, mpc.Hc
ng = length(mpc.con.i_g)
nc, neq = mpc.con.nc, mpc.con.neq
nZ̃, nU, nŶ, nX̂, nK = length(mpc.Z̃), Hp*nu, Hp*ny, Hp*nx̂, Hp*nk
nZ̃, nU, nŶ, nX̂, nK = length(mpc.Z̃), Hp*nu, Hp*ny, Hp*nx̂, Hp*nk̄
nΔŨ, nUe, nŶe = nu*Hc + nϵ, nU + nu, nŶ + ny
strict = Val(true)
myNaN = convert(JNT, NaN)
Expand Down Expand Up @@ -949,12 +949,12 @@ function get_nonlincon_oracle(mpc::NonLinMPC, ::JuMP.GenericModel{JNT}) where JN
transcription = mpc.transcription
jac, hess = mpc.jacobian, mpc.hessian
nu, ny, nx̂, nϵ = model.nu, model.ny, mpc.estim.nx̂, mpc.nϵ
nk = get_nk(model, transcription)
nk̄ = get_nk̄(model, transcription)
Hp, Hc = mpc.Hp, mpc.Hc
i_g = findall(mpc.con.i_g) # convert to non-logical indices for non-allocating @views
ng, ngi = length(mpc.con.i_g), sum(mpc.con.i_g)
nc, neq = mpc.con.nc, mpc.con.neq
nZ̃, nU, nŶ, nX̂, nK = length(mpc.Z̃), Hp*nu, Hp*ny, Hp*nx̂, Hp*nk
nZ̃, nU, nŶ, nX̂, nK = length(mpc.Z̃), Hp*nu, Hp*ny, Hp*nx̂, Hp*nk̄
nΔŨ, nUe, nŶe = nu*Hc + nϵ, nU + nu, nŶ + ny
strict = Val(true)
myNaN, myInf = convert(JNT, NaN), convert(JNT, Inf)
Expand Down
68 changes: 34 additions & 34 deletions src/controller/transcription.jl
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Expand Up @@ -516,15 +516,15 @@ The matrices ``\mathbf{E_S}`` and ``\mathbf{K_S}`` are defined in the Extended H
\vdots & \vdots & \vdots & \vdots & \ddots & \vdots & \vdots & \vdots & \vdots \\
\mathbf{0} & \mathbf{0} & \mathbf{0} & \mathbf{0} & \cdots & λ_o\mathbf{I} & \mathbf{0} & \mathbf{-I} & \mathbf{0} \\
\mathbf{0} & \mathbf{0} & \mathbf{0} & \mathbf{0} & \cdots & \mathbf{0} & \mathbf{A_s} & \mathbf{0} & \mathbf{-I} \end{bmatrix} \\
\mathbf{E_{S}^{k}} &= \begin{bmatrix}
\mathbf{E_{S}^{k̄}} &= \begin{bmatrix}
\mathbf{C_o} & \mathbf{0} & \cdots & \mathbf{0} \\
\mathbf{0} & \mathbf{0} & \cdots & \mathbf{0} \\
\mathbf{0} & \mathbf{C_o} & \cdots & \mathbf{0} \\
\mathbf{0} & \mathbf{0} & \cdots & \mathbf{0} \\
\vdots & \vdots & \ddots & \vdots \\
\mathbf{0} & \mathbf{0} & \cdots & \mathbf{C_o} \\
\mathbf{0} & \mathbf{0} & \cdots & \mathbf{0} \end{bmatrix} \\
\mathbf{E_S} &= \begin{bmatrix} \mathbf{E_{S}^{Δu}} & \mathbf{E_{S}^{x̂}} & \mathbf{E_{S}^{k}} \end{bmatrix} \\
\mathbf{E_S} &= \begin{bmatrix} \mathbf{E_{S}^{Δu}} & \mathbf{E_{S}^{x̂}} & \mathbf{E_{S}^{k̄}} \end{bmatrix} \\
\mathbf{K_S} &= \begin{bmatrix}
λ_o\mathbf{I} & \mathbf{0} \\
\mathbf{0} & \mathbf{A_s} \\
Expand Down Expand Up @@ -558,7 +558,7 @@ function init_defectmat_orthocolloc(
Hp, Hc, Co, λo, As, nxs
) where {NT<:Real}
nu, nx, nd, nx̂ = model.nu, model.nx, model.nd, estim.nx̂
nk = get_nk(model, transcription)
nk̄ = get_nk̄(model, transcription)
λo_I = λo*I(nx)
# --- current state estimates x̂0 ---
KS = zeros(NT, nx̂*Hp, nx̂)
Expand All @@ -577,8 +577,8 @@ function init_defectmat_orthocolloc(
ESx̂[iRow_λo, iCol_λo] = λo_I
ESx̂[iRow_As, iCol_As] = As
end
ESk = repeatdiag([Co; zeros(NT, nxs, nk)], Hp)
ES = [ESΔu ESx̂ ESk]
ESk̄ = repeatdiag([Co; zeros(NT, nxs, nk̄)], Hp)
ES = [ESΔu ESx̂ ESk̄]
# --- current measured disturbances d0 and predictions D̂0 ---
GS = zeros(NT, nx̂*Hp, nd)
JS = zeros(NT, nx̂*Hp, nd*Hp)
Expand Down Expand Up @@ -1027,36 +1027,36 @@ It warm-starts the solver at:
\mathbf{x̂_0}(k+H_p-2|k-1) \\
\mathbf{x̂_0}(k+H_p-1|k-1) \\
\mathbf{x̂_0}(k+H_p-1|k-1) \\
\mathbf{k}(k+0|k-1) \\
\mathbf{k}(k+1|k-1) \\
\mathbf{k̄}(k+0|k-1) \\
\mathbf{k̄}(k+1|k-1) \\
\vdots \\
\mathbf{k}(k+H_p-3|k-1) \\
\mathbf{k}(k+H_p-2|k-1) \\
\mathbf{k}(k+H_p-2|k-1) \\
\mathbf{k̄}(k+H_p-3|k-1) \\
\mathbf{k̄}(k+H_p-2|k-1) \\
\mathbf{k̄}(k+H_p-2|k-1) \\
ϵ_{k-1}
\end{bmatrix}
```
where ``\mathbf{x̂_0}(k+j|k-1)`` is the predicted state for time ``k+j`` computed at the
last control period ``k-1``, expressed as a deviation from the operating point
``\mathbf{x̂_{op}}``. The vector ``\mathbf{k}(k+j|k-1)`` include the ``n_o`` intermediate
``\mathbf{x̂_{op}}``. The vector ``\mathbf{k̄}(k+j|k-1)`` include the ``n_o`` intermediate
stage predictions for the interval ``k+j``, and is also computed at the last control period.
"""
function set_warmstart_mpc!(
mpc::PredictiveController, transcription::OrthogonalCollocation, Z̃var
)
nu, nx̂ = mpc.estim.model.nu, mpc.estim.nx̂
Hp, Hc, Z̃s = mpc.Hp, mpc.Hc, mpc.buffer.Z̃
nk = get_nk(mpc.estim.model, transcription)
nΔU, nX̂, nK = nu*Hc, nx̂*Hp, nk*Hp
nk̄ = get_nk̄(mpc.estim.model, transcription)
nΔU, nX̂, nK = nu*Hc, nx̂*Hp, nk̄*Hp
# --- input increments ΔU ---
Z̃s[1:(nΔU-nu)] .= @views mpc.Z̃[(nu+1):(nΔU)]
Z̃s[(nΔU-nu+1):(nΔU)] .= 0
# --- predicted states X̂0 ---
Z̃s[(nΔU+1):(nΔU+nX̂-nx̂)] .= @views mpc.Z̃[(nΔU+nx̂+1):(nΔU+nX̂)]
Z̃s[(nΔU+nX̂-nx̂+1):(nΔU+nX̂)] .= @views mpc.Z̃[(nΔU+nX̂-nx̂+1):(nΔU+nX̂)]
# --- collocation points K ---
Z̃s[(nΔU+nX̂+1):(nΔU+nX̂+nK-nk)] .= @views mpc.Z̃[(nΔU+nX̂+nk+1):(nΔU+nX̂+nK)]
Z̃s[(nΔU+nX̂+nK-nk+1):(nΔU+nX̂+nK)] .= @views mpc.Z̃[(nΔU+nX̂+nK-nk+1):(nΔU+nX̂+nK)]
Z̃s[(nΔU+nX̂+1):(nΔU+nX̂+nK-nk̄)] .= @views mpc.Z̃[(nΔU+nX̂+nk̄+1):(nΔU+nX̂+nK)]
Z̃s[(nΔU+nX̂+nK-nk̄+1):(nΔU+nX̂+nK)] .= @views mpc.Z̃[(nΔU+nX̂+nK-nk̄+1):(nΔU+nX̂+nK)]
# --- slack variable ϵ ---
mpc.nϵ == 1 && (Z̃s[end] = mpc.Z̃[end])
JuMP.set_start_value.(Z̃var, Z̃s)
Expand Down Expand Up @@ -1169,16 +1169,16 @@ function predict!(
mpc::PredictiveController, model::NonLinModel, ::SingleShooting,
U0, _
)
nu, nx̂, ny, nd, nk, Hp = model.nu, mpc.estim.nx̂, model.ny, model.nd, model.nk, mpc.Hp
nu, nx̂, ny, nd, nk̄, Hp = model.nu, mpc.estim.nx̂, model.ny, model.nd, model.nk̄, mpc.Hp
D̂0 = mpc.D̂0
x̂0 = @views mpc.estim.x̂0[1:nx̂]
d̂0 = @views mpc.d0[1:nd]
for j=1:Hp
u0 = @views U0[(1 + nu*(j-1)):(nu*j)]
û0 = @views Û0[(1 + nu*(j-1)):(nu*j)]
k = @views K[(1 + nk*(j-1)):(nk*j)]
k̄ = @views K[(1 + nk̄*(j-1)):(nk̄*j)]
x̂0next = @views X̂0[(1 + nx̂*(j-1)):(nx̂*j)]
f̂!(x̂0next, û0, k, mpc.estim, model, x̂0, u0, d̂0)
f̂!(x̂0next, û0, k̄, mpc.estim, model, x̂0, u0, d̂0)
x̂0 = @views X̂0[(1 + nx̂*(j-1)):(nx̂*j)]
d̂0 = @views D̂0[(1 + nd*(j-1)):(nd*j)]
ŷ0 = @views Ŷ0[(1 + ny*(j-1)):(ny*j)]
Expand Down Expand Up @@ -1338,7 +1338,7 @@ function con_nonlinprogeq!(
mpc::PredictiveController, model::NonLinModel, transcription::MultipleShooting,
U0, Z̃
)
nx̂, nx, nu, nd, nk = mpc.estim.nx̂, model.nx, model.nu, model.nd, model.nk
nx̂, nx, nu, nd, nk̄ = mpc.estim.nx̂, model.nx, model.nu, model.nd, model.nk̄
Hp, Hc = mpc.Hp, mpc.Hc
nΔU, nX̂ = nu*Hc, nx̂*Hp
f_threads = transcription.f_threads
Expand All @@ -1354,11 +1354,11 @@ function con_nonlinprogeq!(
d̂0 = @views D̂0[(1 + nd*(j-2)):(nd*(j-1))]
end
û0 = @views Û0[(1 + nu*(j-1)):(nu*j)]
k = @views K[(1 + nk*(j-1)):(nk*j)]
k̄ = @views K[(1 + nk̄*(j-1)):(nk̄*j)]
x̂dnext = @views X̂0[(1 + nx̂*(j-1)):(nx̂*(j-1) + nx)]
x̂dnext_Z̃ = @views X̂0_Z̃[(1 + nx̂*(j-1)):(nx̂*(j-1) + nx)]
ŝdnext = @views geq[(1 + nx*(j-1)):(nx*j)]
f!(x̂dnext, k, model, x̂d_Z̃, û0, d̂0, model.p)
f!(x̂dnext, k̄, model, x̂d_Z̃, û0, d̂0, model.p)
ŝdnext .= @. x̂dnext - x̂dnext_Z̃
end
return geq
Expand Down Expand Up @@ -1400,7 +1400,7 @@ function con_nonlinprogeq!(
nΔU, nX̂ = nu*Hc, nx̂*Hp
f_threads = transcription.f_threads
Ts = model.Ts
nk = get_nk(model, transcription)
nk̄ = get_nk̄(model, transcription)
D̂0 = mpc.D̂0
X̂0_Z̃ = @views Z̃[(nΔU+1):(nΔU+nX̂)]
Û0 = disturbedinput!(Û0, mpc.estim, mpc.estim.x̂0, X̂0_Z̃, U0)
Expand All @@ -1413,7 +1413,7 @@ function con_nonlinprogeq!(
d̂0 = @views D̂0[(1 + nd*(j-2)):(nd*(j-1))]
end
û0 = @views Û0[(1 + nu*(j-1)):(nu*j)]
k̇ = @views K̇[(1 + nk*(j-1)):(nk*j)]
k̇ = @views K̇[(1 + nk̄*(j-1)):(nk̄*j)]
d̂0next = @views D̂0[(1 + nd*(j-1)):(nd*j)]
x̂dnext_Z̃ = @views X̂0_Z̃[(1 + nx̂*(j-1)):(nx̂*(j-1) + nx)]
ŝdnext = @views geq[(1 + nx*(j-1)):(nx*(j-1) + nx)]
Expand All @@ -1423,7 +1423,7 @@ function con_nonlinprogeq!(
# last iteration (j-1) may not be executed (iterations are re-orderable)
model.f!(k̇1, x̂d_Z̃, û0, d̂0, model.p)
else
k̇1 .= @views K̇[(1 + nk*(j-1)-nx):(nk*(j-1))] # k2 of of the last iter. j-1
k̇1 .= @views K̇[(1 + nk̄*(j-1)-nx):(nk̄*(j-1))] # k2 of of the last iter. j-1
end
if h < 1
model.f!(k̇2, x̂dnext_Z̃, û0, d̂0next, model.p)
Expand Down Expand Up @@ -1478,9 +1478,9 @@ function con_nonlinprogeq!(
nΔU, nX̂ = nu*Hc, nx̂*Hp
f_threads = transcription.f_threads
Mo, no, τ = mpc.Mo, transcription.no, transcription.τ
nk = get_nk(model, transcription)
nk̄ = get_nk̄(model, transcription)
D̂0 = mpc.D̂0
X̂0_Z̃, K_Z̃ = @views Z̃[(nΔU+1):(nΔU+nX̂)], Z̃[(nΔU+nX̂+1):(nΔU+nX̂+nk*Hp)]
X̂0_Z̃, K_Z̃ = @views Z̃[(nΔU+1):(nΔU+nX̂)], Z̃[(nΔU+nX̂+1):(nΔU+nX̂+nk̄*Hp)]
D̂temp = mpc.buffer.D̂
Û0 = disturbedinput!(Û0, mpc.estim, mpc.estim.x̂0, X̂0_Z̃, U0)
@threadsif f_threads for j=1:Hp
Expand All @@ -1492,23 +1492,23 @@ function con_nonlinprogeq!(
d̂0 = @views D̂0[(1 + nd*(j-2)):(nd*(j-1))]
end
û0 = @views Û0[(1 + nu*(j-1)):(nu*j)]
k̇ = @views K̇[(1 + nk*(j-1)):(nk*j)]
k_Z̃ = @views K_Z̃[(1 + nk*(j-1)):(nk*j)]
k̄dot = @views K̇[(1 + nk̄*(j-1)):(nk̄*j)]
k̄_Z̃ = @views K_Z̃[(1 + nk̄*(j-1)):(nk̄*j)]
d̂0next = @views D̂0[(1 + nd*(j-1)):(nd*j)]
ŝk = @views geq[(1 + nk*(j-1)):(nk*j)]
ŝk = @views geq[(1 + nk̄*(j-1)):(nk̄*j)]
# ----------------- collocation constraint defects -----------------------------
Δk = k̇
Δk = k̄dot
for i=1:no
Δk[(1 + (i-1)*nx):(i*nx)] = @views k_Z̃[(1 + (i-1)*nx):(i*nx)] .- x̂d_Z̃
Δk[(1 + (i-1)*nx):(i*nx)] = @views k̄_Z̃[(1 + (i-1)*nx):(i*nx)] .- x̂d_Z̃
end
mul!(ŝk, Mo, Δk)
d̂i = @views D̂temp[(1 + nd*(j-1)):(nd*j)]
if h > 0
ûi = similar(û0) # TODO: remove this allocation
end
for i=1:no
k̇i = @views k̇[(1 + (i-1)*nx):(i*nx)]
ki_Z̃ = @views k_Z̃[(1 + (i-1)*nx):(i*nx)]
k̇i = @views k̄dot[(1 + (i-1)*nx):(i*nx)]
ki_Z̃ = @views k̄_Z̃[(1 + (i-1)*nx):(i*nx)]
d̂i .= (1-τ[i]).*d̂0 .+ τ[i].*d̂0next
if h < 1
model.f!(k̇i, ki_Z̃, û0, d̂i, model.p)
Expand All @@ -1519,7 +1519,7 @@ function con_nonlinprogeq!(
model.f!(k̇i, ki_Z̃, ûi, d̂i, model.p)
end
end
ŝk .-= k̇
ŝk .-= k̄dot
end
return geq
end
Expand Down
12 changes: 6 additions & 6 deletions src/estimator/construct.jl
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Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
struct StateEstimatorBuffer{NT<:Real}
u ::Vector{NT}
û ::Vector{NT}
k ::Vector{NT}
k̄ ::Vector{NT}
x̂ ::Vector{NT}
Z̃ ::Vector{NT}
V̂ ::Vector{NT}
Expand All @@ -21,7 +21,7 @@ end

@doc raw"""
StateEstimatorBuffer{NT}(
nu::Int, nx̂::Int, nym::Int, ny::Int, nd::Int, nk::Int=0
nu::Int, nx̂::Int, nym::Int, ny::Int, nd::Int, nk̄::Int=0
He::Int=0, nŵ::Int=nx̂, nε::Int=0,
transcription::TranscriptionMethod = SingleShooting()
)
Expand All @@ -31,15 +31,15 @@ Create a buffer for `StateEstimator` objects for estimated states and measured o
The buffer is used to store intermediate results during estimation without allocating.
"""
function StateEstimatorBuffer{NT}(
nu::Int, nx̂::Int, nym::Int, ny::Int, nd::Int, nk::Int=0,
nu::Int, nx̂::Int, nym::Int, ny::Int, nd::Int, nk̄::Int=0,
He::Int=0, nŵ::Int=nx̂, nε::Int=0,
transcription::TranscriptionMethod = SingleShooting()
) where NT <: Real
nZ̃ = nε + get_nZ_mhe(transcription, He, nx̂, nk, nŵ)
nZ̃ = nε + get_nZ_mhe(transcription, He, nx̂, nk̄, nŵ)
nV̂, nŴ, nX̂, nŶ, nD = nym*He, nŵ*He, nx̂*He, ny*He, nd*(He+1)
u = Vector{NT}(undef, nu)
û = Vector{NT}(undef, nu)
k = Vector{NT}(undef, nk)
k̄ = Vector{NT}(undef, nk̄)
x̂ = Vector{NT}(undef, nx̂)
Z̃ = Vector{NT}(undef, nZ̃)
V̂ = Vector{NT}(undef, nV̂)
Expand All @@ -56,7 +56,7 @@ function StateEstimatorBuffer{NT}(
d = Vector{NT}(undef, nd)
empty = Vector{NT}(undef, 0)
return StateEstimatorBuffer{NT}(
u, û, k, x̂, Z̃, V̂, Ŵ, X̂, Ŷ, D, P̂, Q̂, R̂, K̂, ym, ŷ, d, empty
u, û, k̄, x̂, Z̃, V̂, Ŵ, X̂, Ŷ, D, P̂, Q̂, R̂, K̂, ym, ŷ, d, empty
)
end

Expand Down
8 changes: 4 additions & 4 deletions src/estimator/internal_model.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -31,7 +31,7 @@ struct InternalModel{NT<:Real, SM<:SimModel} <: StateEstimator{NT}
function InternalModel{NT}(
model::SM, i_ym, Asm, Bsm, Csm, Dsm
) where {NT<:Real, SM<:SimModel}
nu, ny, nd, nk = model.nu, model.ny, model.nd, model.nk
nu, ny, nd, nk̄ = model.nu, model.ny, model.nd, model.nk̄
nym, nyu = validate_ym(model, i_ym)
validate_internalmodel(model, nym, Csm, Dsm)
As, Bs, Cs, Ds = stoch_ym2y(model, i_ym, Asm, Bsm, Csm, Dsm)
Expand All @@ -46,7 +46,7 @@ struct InternalModel{NT<:Real, SM<:SimModel} <: StateEstimator{NT}
ŷs = zeros(NT, ny)
direct = true # InternalModel always uses direct transmission from ym
prepared = [false]
buffer = StateEstimatorBuffer{NT}(nu, nx̂, nym, ny, nd, nk)
buffer = StateEstimatorBuffer{NT}(nu, nx̂, nym, ny, nd, nk̄)
return new{NT, SM}(
model,
x̂op, f̂op, x̂0, x̂d, x̂s, ŷs, x̂snext,
Expand Down Expand Up @@ -302,8 +302,8 @@ function update_estimate!(estim::InternalModel, u0, _ , d0)
model = estim.model
x̂d, x̂s, ŷs = estim.x̂d, estim.x̂s, estim.ŷs
# -------------- deterministic model ---------------------
x̂dnext, û0, k = estim.buffer.x̂, estim.buffer.û, estim.buffer.k
f̂!(x̂dnext, û0, k, estim, estim.model, x̂d, u0, d0)
x̂dnext, û0, k̄ = estim.buffer.x̂, estim.buffer.û, estim.buffer.k̄
f̂!(x̂dnext, û0, k̄, estim, estim.model, x̂d, u0, d0)
x̂d .= x̂dnext # this also updates estim.x̂0 (they are the same object)
# --------------- stochastic model -----------------------
x̂snext = estim.x̂snext
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