removed NonLinMHE: starting to use branch for new features

Author: franckgaga

README.md

@@ -114,7 +114,8 @@ for more detailed examples.
   - [x] extended Kalman filter
   - [x] unscented Kalman filter
   - [ ] moving horizon estimator
-- [x] observers in predictor form to ease  control applications
+- [x] automatic model augmentation for offset-free tracking
+- [x] observers in predictor form to ease control applications
 - [ ] moving horizon estimator that supports:
   - [ ] inequality state constraints
   - [ ] zero process noise equality constraint to reduce the problem size

example/juMPC.jl

@@ -57,11 +57,8 @@ updatestate!(luenberger, [0,0], [0,0])
 
 mpcluen = LinMPC(luenberger)
 
-mhe = NonLinMHE(nonLinModel2)
 
-updatestate!(mhe, [0, 0], [0, 0], [1])
 
-#=
 kalmanFilter1 = KalmanFilter(linModel1)
 kalmanFilter2 = KalmanFilter(linModel1,nint_ym=0)
 
@@ -213,4 +210,3 @@ display(pu)
 display(py)
 
 =#
-=#

src/ModelPredictiveControl.jl

@@ -14,7 +14,6 @@ import OSQP, Ipopt
 export SimModel, LinModel, NonLinModel, setop!, setstate!, updatestate!, evaloutput
 export StateEstimator, InternalModel, Luenberger
 export SteadyKalmanFilter, KalmanFilter, UnscentedKalmanFilter, ExtendedKalmanFilter
-export NonLinMHE
 export initstate!
 export PredictiveController, LinMPC, NonLinMPC, setconstraint!, moveinput!, getinfo, sim!
 

src/estimator/mhe.jl

@@ -303,7 +303,6 @@ end
 include("estimator/kalman.jl")
 include("estimator/luenberger.jl")
 include("estimator/internal_model.jl")
-include("estimator/mhe.jl")
 
 "Get [`InternalModel`](@ref) output `ŷ` from current measured outputs `ym` and dist. `d`."
 evalŷ(estim::InternalModel, ym, d) = evaloutput(estim,ym, d)