Merge branch 'main' into Ipopt_VectorNonlinearOracle

Author: franckgaga

Project.toml

@@ -1,7 +1,7 @@
 name = "ModelPredictiveControl"
 uuid = "61f9bdb8-6ae4-484a-811f-bbf86720c31c"
 authors = ["Francis Gagnon"]
-version = "1.11.0"
+version = "1.11.1"
 
 [deps]
 ControlSystemsBase = "aaaaaaaa-a6ca-5380-bf3e-84a91bcd477e"

src/model/linmodel.jl

@@ -135,10 +135,15 @@ LinModel with a sample time Ts = 0.1 s:
     state-space representation. 
     
     It is assumed that ``\mathbf{D_u=0}`` (or `sys` is strictly proper) since otherwise the
-    resulting discrete controller is acausal by definition. Indeed, all discrete controllers
-    (1) sample an output ``\mathbf{y}(k)`` from the plant, (2) computes an action 
-    ``\mathbf{u}(k)`` and (3) apply the action on the plant. There is a causality paradox
-    if ``\mathbf{u}(k)`` impacts ``\mathbf{y}(k)`` even before computing it.
+    resulting discrete controller would be acausal in its implementation (≠ mathematical
+    causality). Indeed, at each sampling instant `k`, the predictive controller will:
+    
+    1. sample an output ``\mathbf{y}(k)`` from the plant
+    2. computes an action ``\mathbf{u}(k)`` and 
+    3. apply the action on the plant. 
+    
+    There is a causality paradox if ``\mathbf{u}(k)`` impacts ``\mathbf{y}(k)`` even before
+    computing it.
 """
 function LinModel(
     sys::StateSpace{E, NT},

src/precompile.jl

@@ -13,35 +13,14 @@ mpc_im.estim()
 u = mpc_im([55, 30])
 sim!(mpc_im, 2)
 
-mpc_kf = setconstraint!(LinMPC(KalmanFilter(model)), ymin=[45, -Inf])
+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])
 
-transcription = MultipleShooting()
-mpc_lo = setconstraint!(LinMPC(Luenberger(model); transcription), ymin=[45, -Inf])
-initstate!(mpc_lo, model.uop, model())
-preparestate!(mpc_lo, [55, 30])
-mpc_lo.estim()
-u = mpc_lo([55, 30])
-sim!(mpc_lo, 2, [55, 30])
-
-mpc_ukf = setconstraint!(LinMPC(UnscentedKalmanFilter(model)), ymin=[45, -Inf])
-initstate!(mpc_ukf, model.uop, model())
-preparestate!(mpc_ukf, [55, 30])
-mpc_ukf.estim()
-u = mpc_ukf([55, 3])
-sim!(mpc_ukf, 2, [55, 30])
-
-mpc_ekf = setconstraint!(LinMPC(ExtendedKalmanFilter(model)), ymin=[45, -Inf])
-initstate!(mpc_ekf, model.uop, model())
-preparestate!(mpc_ekf, [55, 30])
-mpc_ekf.estim()
-u = mpc_ekf([55, 30])
-sim!(mpc_ekf, 2, [55, 30])
-
 mpc_skf = setconstraint!(LinMPC(SteadyKalmanFilter(model)), ymin=[45, -Inf])
 initstate!(mpc_skf, model.uop, model())
 preparestate!(mpc_skf, [55, 30])
@@ -98,12 +77,6 @@ nlmodel = setop!(
     uop=[10, 10], yop=[50, 30]
 )
 y = nlmodel()
-nmpc_im = setconstraint!(NonLinMPC(InternalModel(nlmodel), Hp=10, Cwt=Inf), ymin=[45, -Inf])
-initstate!(nmpc_im, nlmodel.uop, y)
-preparestate!(nmpc_im, [55, 30])
-nmpc_im.estim()
-u = nmpc_im([55, 30])
-sim!(nmpc_im, 2, [55, 30])
 
 transcription = MultipleShooting(f_threads=true)
 nmpc_ukf = setconstraint!(NonLinMPC(