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added: slightly improve the speed of precompilation #311

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Merged
franckgaga merged 2 commits into from
Feb 6, 2026
Merged

added: slightly improve the speed of precompilation #311

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8b58f36
added: trying to improve the speed of precompilation
franckgaga Feb 5, 2026
f62d591
debug: precompile file
franckgaga Feb 5, 2026
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198 changes: 101 additions & 97 deletions src/precompile.jl
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Original file line number Diff line number Diff line change
@@ -1,64 +1,13 @@
# Putting some things in `@setup_workload` instead of `@compile_workload` can reduce the
# size of the precompile file and potentially make loading faster.
@setup_workload begin

sys = [
tf(1.90, [1800, 1]) tf(1.90, [1800, 1]);
tf(-0.74,[800, 1]) tf(0.74, [800, 1])
]
Ts = 400.0
model = setop!(LinModel(sys, Ts), uop=[10, 10], yop=[50, 30])
y = model()

mpc_im = setconstraint!(LinMPC(InternalModel(model)), ymin=[45, -Inf])
initstate!(mpc_im, model.uop, y)
preparestate!(mpc_im, [55, 30])
mpc_im.estim()
u = mpc_im([55, 30])
sim!(mpc_im, 2)

transcription = MultipleShooting()
mpc_kf = setconstraint!(LinMPC(KalmanFilter(model); transcription), ymin=[45, -Inf])
initstate!(mpc_kf, model.uop, model())
preparestate!(mpc_kf, [55, 30])
mpc_kf.estim()
u = mpc_kf([55, 30])
sim!(mpc_kf, 2, [55, 30])

mpc_skf = setconstraint!(LinMPC(SteadyKalmanFilter(model)), ymin=[45, -Inf])
initstate!(mpc_skf, model.uop, model())
preparestate!(mpc_skf, [55, 30])
mpc_skf.estim()
u = mpc_skf([55, 30])
sim!(mpc_skf, 2, [55, 30])

mpc_mhe = setconstraint!(LinMPC(MovingHorizonEstimator(model, He=2)), ymin=[45, -Inf])
setconstraint!(mpc_mhe.estim, x̂min=[-50,-50,-50,-50], x̂max=[50,50,50,50])
initstate!(mpc_mhe, model.uop, model())
preparestate!(mpc_mhe, [55, 30])
mpc_mhe.estim()
u = mpc_mhe([55, 30])
sim!(mpc_mhe, 2, [55, 30])

mpc_man = setconstraint!(LinMPC(ManualEstimator(model)), ymin=[45, -Inf])
initstate!(mpc_man, model.uop, model())
setstate!(mpc_man, ones(4))
u = mpc_man([55, 30])

nmpc_skf = setconstraint!(NonLinMPC(SteadyKalmanFilter(model), Cwt=Inf), ymin=[45, -Inf])
initstate!(nmpc_skf, model.uop, model())
preparestate!(nmpc_skf, [55, 30])
nmpc_skf.estim()
u = nmpc_skf([55, 30])
sim!(nmpc_skf, 2, [55, 30])

res = sim!(model, 2)

res_man = SimResult(model, res.U_data, res.Y_data; X_data=res.X_data)

exmpc = ExplicitMPC(model)
initstate!(exmpc, model.uop, model())
preparestate!(exmpc, [55, 30])
exmpc.estim()
u = exmpc([55, 30])
sim!(exmpc, 2, [55, 30])

sys2 = minreal(ss(sys))
function f!(xnext, x, u, _ , p)
A, B, _ = p
mul!(xnext, A , x)
Expand All @@ -70,51 +19,106 @@ function h!(y, x, _ , p)
mul!(y, C, x)
return nothing
end

sys2 = minreal(ss(sys))
nlmodel = setop!(
NonLinModel(f!, h!, Ts, 2, 2, 2, solver=RungeKutta(4), p=(sys2.A, sys2.B, sys2.C)),
uop=[10, 10], yop=[50, 30]
)
y = nlmodel()

transcription = MultipleShooting(f_threads=true)
nmpc_ukf = setconstraint!(NonLinMPC(
UnscentedKalmanFilter(nlmodel); Hp=10, transcription, Cwt=1e3), ymin=[45, -Inf]
)
initstate!(nmpc_ukf, nlmodel.uop, y)
preparestate!(nmpc_ukf, [55, 30])
u = nmpc_ukf([55, 30])
sim!(nmpc_ukf, 2, [55, 30])

nmpc_ekf = setconstraint!(NonLinMPC(ExtendedKalmanFilter(model), Cwt=Inf), ymin=[45, -Inf])
initstate!(nmpc_ekf, model.uop, model())
preparestate!(nmpc_ekf, [55, 30])
u = nmpc_ekf([55, 30])
sim!(nmpc_ekf, 2, [55, 30])

transcription = TrapezoidalCollocation()
nmpc_mhe = setconstraint!(NonLinMPC(
MovingHorizonEstimator(nlmodel, He=2); transcription, Hp=10, Cwt=Inf), ymin=[45, -Inf]
)
setconstraint!(nmpc_mhe.estim, x̂min=[-50,-50,-50,-50], x̂max=[50,50,50,50])
initstate!(nmpc_mhe, nlmodel.uop, y)
preparestate!(nmpc_mhe, [55, 30])
u = nmpc_mhe([55, 30])
sim!(nmpc_mhe, 2, [55, 30])
p = (sys2.A, sys2.B, sys2.C)

function JE( _ , Ŷe, _ , R̂y)
Ŷ = @views Ŷe[3:end]
Ȳ = R̂y - Ŷ
return dot(Ȳ, Ȳ)
end
R̂y = repeat([55; 30], 10)
empc = setconstraint!(
NonLinMPC(nlmodel, Mwt=[0, 0], Hp=10, Cwt=Inf, Ewt=1, JE=JE, p=R̂y), ymin=[45, -Inf]
)
preparestate!(empc, [55, 30])
u = empc()
sim!(empc, 2)

linearizemodel = linearize(nlmodel)
setmodel!(mpc_kf, linearizemodel)

# all calls in this block will be precompiled, regardless of whether
# they belong to your package or not (on Julia 1.8 and higher)
@compile_workload begin

model = setop!(LinModel(sys, Ts), uop=[10, 10], yop=[50, 30])
y = model()

mpc_im = setconstraint!(LinMPC(InternalModel(model)), ymin=[45, -Inf])
initstate!(mpc_im, model.uop, y)
preparestate!(mpc_im, [55, 30])
mpc_im.estim()
u = mpc_im([55, 30])
sim!(mpc_im, 2)

mpc_skf = setconstraint!(LinMPC(SteadyKalmanFilter(model)), ymin=[45, -Inf])
initstate!(mpc_skf, model.uop, model())
preparestate!(mpc_skf, [55, 30])
mpc_skf.estim()
u = mpc_skf([55, 30])
sim!(mpc_skf, 2, [55, 30])

transcription = MultipleShooting()
mpc_kf = setconstraint!(LinMPC(KalmanFilter(model); transcription), ymin=[45, -Inf])
initstate!(mpc_kf, model.uop, model())
preparestate!(mpc_kf, [55, 30])
mpc_kf.estim()
u = mpc_kf([55, 30])
sim!(mpc_kf, 2, [55, 30])

mhe = MovingHorizonEstimator(model, He=2, direct=true)
mhe = setconstraint!(mhe, x̂min=[-50,-50,-50,-50], x̂max=[50,50,50,50])
initstate!(mhe, model.uop, model())
preparestate!(mhe, [55, 30])
mhe()

mpc_man = setconstraint!(LinMPC(ManualEstimator(model)), ymin=[45, -Inf])
initstate!(mpc_man, model.uop, model())
setstate!(mpc_man, ones(4))
mpc_man([55, 30])

nmpc_skf = setconstraint!(NonLinMPC(SteadyKalmanFilter(model), Cwt=Inf), ymin=[45, -Inf])
initstate!(nmpc_skf, model.uop, model())
preparestate!(nmpc_skf, [55, 30])
nmpc_skf.estim()
nmpc_skf([55, 30])

res = sim!(model, 2)
res_man = SimResult(model, res.U_data, res.Y_data; X_data=res.X_data)

exmpc = ExplicitMPC(model)
initstate!(exmpc, model.uop, model())
preparestate!(exmpc, [55, 30])
exmpc.estim()
exmpc([55, 30])

nlmodel = NonLinModel(f!, h!, Ts, 2, 2, 2; solver=RungeKutta(4), p)
nlmodel = setop!(nlmodel, uop=[10, 10], yop=[50, 30])
y = nlmodel()

transcription = MultipleShooting(f_threads=true)
nmpc_ukf = setconstraint!(NonLinMPC(
UnscentedKalmanFilter(nlmodel); Hp=10, transcription, Cwt=1e3), ymin=[45, -Inf]
)
initstate!(nmpc_ukf, nlmodel.uop, y)
preparestate!(nmpc_ukf, [55, 30])
nmpc_ukf([55, 30])
sim!(nmpc_ukf, 2, [55, 30])

nmpc_ekf = setconstraint!(NonLinMPC(ExtendedKalmanFilter(model), Cwt=Inf), ymin=[45, -Inf])
initstate!(nmpc_ekf, model.uop, model())
preparestate!(nmpc_ekf, [55, 30])
nmpc_ekf([55, 30])

transcription = TrapezoidalCollocation()
nmpc_mhe = setconstraint!(NonLinMPC(
MovingHorizonEstimator(nlmodel, He=2); transcription, Hp=10, Cwt=Inf), ymin=[45, -Inf]
)
setconstraint!(nmpc_mhe.estim, x̂min=[-50,-50,-50,-50], x̂max=[50,50,50,50])
initstate!(nmpc_mhe, nlmodel.uop, y)
preparestate!(nmpc_mhe, [55, 30])
nmpc_mhe([55, 30])

empc = setconstraint!(
NonLinMPC(nlmodel, Mwt=[0, 0], Hp=10, Cwt=Inf, Ewt=1, JE=JE, p=R̂y), ymin=[45, -Inf]
)
preparestate!(empc, [55, 30])
empc()

linearizemodel = linearize(nlmodel)
setmodel!(mpc_kf, linearizemodel)

end

end # @setup_workload