test: added tests for ManualEstimator

Author: franckgaga

src/estimator/manual.jl

@@ -88,33 +88,38 @@ ManualEstimator estimator with a sample time Ts = 0.5 s, LinModel and:
 # Extended Help
 !!! details "Extended Help"
     A first use case is a linear predictive controller based on nonlinear state estimation,
-    for example a nonlinear [`MovingHorizonEstimator`](@ref) (MHE). The [`ManualEstimator`](@ref)
-    serves as a wrapper to provide the minimal required information to construct any
-    [`PredictiveController`](@ref) object, e.g.:
+    for example a nonlinear [`MovingHorizonEstimator`](@ref) (MHE). Note that the model
+    augmentation scheme with `nint_u` and `nint_ym` options must be identical for both
+    [`StateEstimator`](@ref) objects (see [`SteadyKalmanFilter`](@ref) extended help for
+    details on augmentation). The [`ManualEstimator`](@ref) serves as a wrapper to provide
+    the minimal required information to construct any [`PredictiveController`](@ref) object:
+
     ```jldoctest
-    f(x,u,_,_) = 0.5*sin.(x + u)
-    h(x,_,_) = x
-    model = NonLinModel(f, h, 10.0, 1, 1, 1, solver=nothing)
-    linModel = linearize(model, x=[0], u=[0])
-    man = ManualEstimator(linModel, nint_u=[1])
-    mpc = LinMPC(man)
-    estim = MovingHorizonEstimator(model, nint_u=[1], He=5)
-    estim = setconstraint!(estim, v̂min=[-0.001], v̂max=[0.001])
-    initstate!(estim, [0], [0])
-    y_data, ŷ_data = zeros(5), zeros(5)
-    for i=1:5
-        y = model()                         # simulated measurement
-        x̂ = preparestate!(estim, y)         # correct nonlinear MHE state estimate
-        ŷ = estim()                         # nonlinear MHE estimated output
-        setstate!(mpc, x̂)                   # update MPC with the MHE corrected state 
-        u = moveinput!(mpc, [0])
-        y_data[i], ŷ_data[i] = y[1], ŷ[1]
-        updatestate!(estim, u, y)           # update nonlinear MHE estimation
-        updatestate!(model, u .+ 0.5)       # update plant simulator with load disturbance
+    julia> function man_sim!()
+        f(x,u,_,_) = 0.5*sin.(x + u)
+        h(x,_,_) = x
+        model = NonLinModel(f, h, 10.0, 1, 1, 1, solver=nothing)
+        linModel = linearize(model, x=[0], u=[0])
+        man = ManualEstimator(linModel, nint_u=[1])
+        mpc = LinMPC(man)
+        estim = MovingHorizonEstimator(model, nint_u=[1], He=5)
+        estim = setconstraint!(estim, v̂min=[-0.001], v̂max=[0.001])
+        initstate!(estim, [0], [0])
+        y_data, ŷ_data = zeros(5), zeros(5)
+        for i=1:5
+            y = model()                         # simulated measurement
+            x̂ = preparestate!(estim, y)         # correct nonlinear MHE state estimate
+            ŷ = estim()                         # nonlinear MHE estimated output
+            setstate!(mpc, x̂)                   # update MPC with the MHE corrected state 
+            u = moveinput!(mpc, [0])
+            y_data[i], ŷ_data[i] = y[1], ŷ[1]
+            updatestate!(estim, u, y)           # update nonlinear MHE estimation
+            updatestate!(model, u .+ 0.5)       # update simulator with load disturbance
+        end
+        return collect([y_data ŷ_data]')
     end
-    YandŶ = collect([y_data ŷ_data]')
 
-    # output
+    julia> YandŶ = man_sim!()
     2×5 Matrix{Float64}:
       0.0          0.239713  0.227556  0.157837  0.0986288
      -1.41242e-19  0.238713  0.226556  0.156837  0.0976288

src/precompile.jl

@@ -56,6 +56,11 @@ 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])