added: PrecompileTools to reduce package latency

Author: franckgaga

Project.toml

@@ -9,6 +9,7 @@ Ipopt = "b6b21f68-93f8-5de0-b562-5493be1d77c9"
 JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 OSQP = "ab2f91bb-94b4-55e3-9ba0-7f65df51de79"
+PrecompileTools = "aea7be01-6a6a-4083-8856-8a6e6704d82a"
 
 [compat]
 ControlSystemsBase = "1"

example/juMPC.jl

@@ -131,6 +131,7 @@ end
 
 @time u_data, y_data, r_data, d_data = test_mpc(linModel4, nmpc)
 
+#=
 using PlotThemes, Plots
 #theme(:default)
 theme(:dark)
@@ -151,4 +152,5 @@ pd = plot(0:N-1,d_data[1,:],label=raw"$d_1$")
 display(pd)
 display(pu)
 display(py)
+=#
 

src/ModelPredictiveControl.jl

@@ -1,5 +1,7 @@
 module ModelPredictiveControl
 
+using PrecompileTools
+
 using LinearAlgebra
 using ControlSystemsBase
 using JuMP
@@ -15,4 +17,14 @@ include("sim_model.jl")
 include("state_estim.jl")
 include("predictive_control.jl")
 
+@setup_workload begin
+    # Putting some things in `@setup_workload` instead of `@compile_workload` can reduce the size of the
+    # precompile file and potentially make loading faster.
+    @compile_workload begin
+        # all calls in this block will be precompiled, regardless of whether
+        # they belong to your package or not (on Julia 1.8 and higher)
+        include("precompile_calls.jl")
+    end
+end
+
 end

src/precompile_calls.jl

@@ -0,0 +1,37 @@
+sys = [ tf(1.90, [18, 1]) tf(1.90, [18, 1]);
+        tf(-0.74,[8, 1])  tf(0.74, [8, 1]) ]
+Ts = 4.0
+model = setop!(LinModel(sys, Ts), uop=[10, 10], yop=[50, 30])
+y = model()
+
+mpc_im = setconstraint!(LinMPC(InternalModel(model)), ŷmin=[45, -Inf])
+initstate!(mpc_im, model.uop, y)
+u = mpc_im([50, 30], ym=y)
+updatestate!(mpc_im, u, y)
+mpc_kf = setconstraint!(LinMPC(KalmanFilter(model)), ŷmin=[45, -Inf])
+initstate!(mpc_kf, model.uop, model())
+u = mpc_kf([50, 30])
+updatestate!(mpc_kf, u, y)
+mpc_ukf = setconstraint!(LinMPC(UnscentedKalmanFilter(model)), ŷmin=[45, -Inf])
+initstate!(mpc_ukf, model.uop, model())
+u = mpc_ukf([50, 30])
+updatestate!(mpc_ukf, u, y)
+mpc_skf = setconstraint!(LinMPC(SteadyKalmanFilter(model)), ŷmin=[45, -Inf])
+initstate!(mpc_skf, model.uop, model())
+u = mpc_skf([50, 30])
+updatestate!(mpc_skf, u, y)
+updatestate!(model, u) 
+
+f(x,u,_) = model.A*x + model.Bu*u
+h(x,_) = model.C*x
+
+nlmodel = setop!(NonLinModel(f, h, Ts, 2, 2, 2))
+y = nlmodel()
+nmpc_im = setconstraint!(NonLinMPC(InternalModel(nlmodel)), ŷmin=[45, -Inf])
+initstate!(nmpc_im, nlmodel.uop, y)
+u = nmpc_im([50, 30], ym=y)
+updatestate!(nmpc_im, u, y)
+nmpc_ukf = setconstraint!(NonLinMPC(UnscentedKalmanFilter(nlmodel)), ŷmin=[45, -Inf])
+initstate!(nmpc_ukf, nlmodel.uop, y)
+u = nmpc_ukf([50, 30])
+updatestate!(nmpc_ukf, u, y)