added ekf to pre-compilation

Author: franckgaga

Project.toml

@@ -1,7 +1,7 @@
 name = "ModelPredictiveControl"
 uuid = "61f9bdb8-6ae4-484a-811f-bbf86720c31c"
 authors = ["Francis Gagnon"]
-version = "0.5.10"
+version = "0.6.0"
 
 [deps]
 ControlSystemsBase = "aaaaaaaa-a6ca-5380-bf3e-84a91bcd477e"

README.md

@@ -111,7 +111,7 @@ for more detailed examples.
   - [x] Kalman filter
   - [x] Luenberger observer
   - [x] internal model structure
-  - [ ] extended Kalman filter
+  - [x] extended Kalman filter
   - [x] unscented Kalman filter
   - [ ] moving horizon estimator
 - [x] observers in predictor form to ease  control applications

example/juMPC.jl

@@ -109,7 +109,7 @@ setconstraint!(mpc, ŷmin=[-Inf,-Inf], ŷmax=[55, 35])
 setconstraint!(mpc, Δumin=[-Inf,-Inf],Δumax=[+Inf,+Inf])
 
 moveinput!(mpc, mpc.estim.model.yop + [0, 2], mpc.estim.model.dop)
-display(getinfo(mpc)[1])
+display(getinfo(mpc)[2])
 
 nmpc = NonLinMPC(kf, Hp=15, Hc=1, Mwt=[1, 1] , Nwt=[0.1, 0.1], Cwt=1e5, Ewt=0.0)
 
@@ -120,7 +120,7 @@ setconstraint!(nmpc, ŷmin=[-Inf,-Inf], ŷmax=[55, 35])
 setconstraint!(nmpc, Δumin=[-Inf,-Inf],Δumax=[+Inf,+Inf])
 
 moveinput!(nmpc, nmpc.estim.model.yop + [0, 2], nmpc.estim.model.dop)
-display(getinfo(nmpc)[1])
+display(getinfo(nmpc)[2])
 
 function test_mpc(model, mpc)
     N = 200 

src/precompile_calls.jl

@@ -6,37 +6,64 @@ y = model()
 
 mpc_im = setconstraint!(LinMPC(InternalModel(model)), ŷmin=[45, -Inf])
 initstate!(mpc_im, model.uop, y)
-u = mpc_im([50, 30], ym=y)
+mpc_im.estim([50, 30])
+u = mpc_im([55, 30], ym=y)
+sim!(mpc_im, 3)
 
 mpc_kf = setconstraint!(LinMPC(KalmanFilter(model)), ŷmin=[45, -Inf])
 initstate!(mpc_kf, model.uop, model())
-u = mpc_kf([50, 30])
-sim!(mpc_kf, 15)
+mpc_kf.estim()
+u = mpc_kf([55, 30])
+sim!(mpc_kf, 3, [55, 30])
+
 mpc_lo = setconstraint!(LinMPC(Luenberger(model)), ŷmin=[45, -Inf])
 initstate!(mpc_lo, model.uop, model())
-u = mpc_lo([50, 30])
-sim!(mpc_lo, 15)
+mpc_lo.estim()
+u = mpc_lo([55, 30])
+sim!(mpc_lo, 3, [55, 30])
+
 mpc_ukf = setconstraint!(LinMPC(UnscentedKalmanFilter(model)), ŷmin=[45, -Inf])
 initstate!(mpc_ukf, model.uop, model())
-u = mpc_ukf([50, 30])
-sim!(mpc_ukf, 15)
+mpc_ukf.estim()
+u = mpc_ukf([55, 3])
+sim!(mpc_ukf, 3, [55, 30])
+
+mpc_ekf = setconstraint!(LinMPC(ExtendedKalmanFilter(model)), ŷmin=[45, -Inf])
+initstate!(mpc_ekf, model.uop, model())
+mpc_ekf.estim()
+u = mpc_ekf([55, 30])
+sim!(mpc_ekf, 3, [55, 30])
+
 mpc_skf = setconstraint!(LinMPC(SteadyKalmanFilter(model)), ŷmin=[45, -Inf])
 initstate!(mpc_skf, model.uop, model())
-u = mpc_skf([50, 30])
-sim!(mpc_skf, 15)
+mpc_skf.estim()
+u = mpc_skf([55, 30])
+sim!(mpc_skf, 3, [55, 30])
 
-sim!(model, 15)
+nmpc_skf = setconstraint!(NonLinMPC(SteadyKalmanFilter(model), Cwt=Inf), ŷmin=[45, -Inf])
+initstate!(nmpc_skf, model.uop, model())
+nmpc_skf.estim()
+u = nmpc_skf([55, 30])
+sim!(nmpc_skf, 3, [55, 30])
+
+sim!(model, 3)
 
 f(x,u,_) = model.A*x + model.Bu*u
 h(x,_) = model.C*x
 
-nlmodel = setop!(NonLinModel(f, h, Ts, 2, 2, 2))
+nlmodel = setop!(NonLinModel(f, h, Ts, 2, 2, 2), uop=[10, 10], yop=[50, 30])
 y = nlmodel()
-nmpc_im = setconstraint!(NonLinMPC(InternalModel(nlmodel)), ŷmin=[45, -Inf])
+nmpc_im = setconstraint!(NonLinMPC(InternalModel(nlmodel), Cwt=Inf), ŷmin=[45, -Inf])
 initstate!(nmpc_im, nlmodel.uop, y)
-u = nmpc_im([50, 30], ym=y)
-sim!(nmpc_im, 15)
-nmpc_ukf = setconstraint!(NonLinMPC(UnscentedKalmanFilter(nlmodel)), ŷmin=[45, -Inf])
+u = nmpc_im([55, 30], ym=y)
+sim!(nmpc_im, 3, [55, 30])
+
+nmpc_ukf = setconstraint!(NonLinMPC(UnscentedKalmanFilter(nlmodel), Cwt=Inf), ŷmin=[45, -Inf])
 initstate!(nmpc_ukf, nlmodel.uop, y)
-u = nmpc_ukf([50, 30])
-sim!(nmpc_ukf, 15)
+u = nmpc_ukf([55, 30])
+sim!(nmpc_ukf, 3, [55, 30])
+
+nmpc_ekf = setconstraint!(NonLinMPC(ExtendedKalmanFilter(model), Cwt=Inf), ŷmin=[45, -Inf])
+initstate!(nmpc_ekf, model.uop, model())
+u = nmpc_ekf([55, 30])
+sim!(nmpc_ekf, 3, [55, 30])