added: estimated states when printing estimators and controllers

Author: franckgaga

src/controller/construct.jl

@@ -47,7 +47,7 @@ LinMPC controller with a sample time Ts = 4.0 s, OSQP optimizer, SteadyKalmanFil
  10 prediction steps Hp
   2 control steps Hc
   1 manipulated inputs u (0 integrating states)
-  2 states x̂
+  2 estimated states x̂
   1 measured outputs ym (1 integrating states)
   0 unmeasured outputs yu
   0 measured disturbances d

src/controller/explicitmpc.jl

@@ -111,7 +111,7 @@ ExplicitMPC controller with a sample time Ts = 4.0 s, SteadyKalmanFilter estimat
  30 prediction steps Hp
   1 control steps Hc
   1 manipulated inputs u (0 integrating states)
-  4 states x̂
+  4 estimated states x̂
   2 measured outputs ym (2 integrating states)
   0 unmeasured outputs yu
   0 measured disturbances d
@@ -150,7 +150,7 @@ ExplicitMPC controller with a sample time Ts = 4.0 s, KalmanFilter estimator and
  30 prediction steps Hp
   1 control steps Hc
   1 manipulated inputs u (0 integrating states)
-  3 states x̂
+  3 estimated states x̂
   1 measured outputs ym (1 integrating states)
   1 unmeasured outputs yu
   0 measured disturbances d

src/controller/linmpc.jl

@@ -135,7 +135,7 @@ LinMPC controller with a sample time Ts = 4.0 s, OSQP optimizer, SteadyKalmanFil
  30 prediction steps Hp
   1 control steps Hc
   1 manipulated inputs u (0 integrating states)
-  4 states x̂
+  4 estimated states x̂
   2 measured outputs ym (2 integrating states)
   0 unmeasured outputs yu
   0 measured disturbances d
@@ -199,7 +199,7 @@ LinMPC controller with a sample time Ts = 4.0 s, OSQP optimizer, KalmanFilter es
  30 prediction steps Hp
   1 control steps Hc
   1 manipulated inputs u (0 integrating states)
-  3 states x̂
+  3 estimated states x̂
   1 measured outputs ym (1 integrating states)
   1 unmeasured outputs yu
   0 measured disturbances d

src/controller/nonlinmpc.jl

@@ -149,7 +149,7 @@ NonLinMPC controller with a sample time Ts = 10.0 s, Ipopt optimizer, UnscentedK
  20 prediction steps Hp
   1 control steps Hc
   1 manipulated inputs u (0 integrating states)
-  2 states x̂
+  2 estimated states x̂
   1 measured outputs ym (1 integrating states)
   0 unmeasured outputs yu
   0 measured disturbances d
@@ -224,7 +224,7 @@ NonLinMPC controller with a sample time Ts = 10.0 s, Ipopt optimizer, UnscentedK
  20 prediction steps Hp
   1 control steps Hc
   1 manipulated inputs u (0 integrating states)
-  2 states x̂
+  2 estimated states x̂
   1 measured outputs ym (1 integrating states)
   0 unmeasured outputs yu
   0 measured disturbances d

src/estimator/internal_model.jl

@@ -65,7 +65,7 @@ sum both values : ``\mathbf{ŷ = ŷ_d + ŷ_s}``.
 julia> estim = InternalModel(LinModel([tf(3, [30, 1]); tf(-2, [5, 1])], 0.5), i_ym=[2])
 InternalModel estimator with a sample time Ts = 0.5 s, LinModel and:
  1 manipulated inputs u
- 2 states x̂
+ 2 estimated states x̂
  1 measured outputs ym
  1 unmeasured outputs yu
  0 measured disturbances d
@@ -266,7 +266,7 @@ function print_estim_dim(io::IO, estim::InternalModel, n)
     nu, nd = estim.model.nu, estim.model.nd
     nx̂, nym, nyu = estim.nx̂, estim.nym, estim.nyu
     println(io, "$(lpad(nu, n)) manipulated inputs u")
-    println(io, "$(lpad(nx̂, n)) states x̂")
+    println(io, "$(lpad(nx̂, n)) estimated states x̂")
     println(io, "$(lpad(nym, n)) measured outputs ym")
     println(io, "$(lpad(nyu, n)) unmeasured outputs yu")
     print(io,   "$(lpad(nd, n)) measured disturbances d")

src/estimator/kalman.jl

@@ -111,7 +111,7 @@ julia> model = LinModel([tf(3, [30, 1]); tf(-2, [5, 1])], 0.5);
 julia> estim = SteadyKalmanFilter(model, i_ym=[2], σR=[1], σQint_ym=[0.01])
 SteadyKalmanFilter estimator with a sample time Ts = 0.5 s, LinModel and:
  1 manipulated inputs u (0 integrating states)
- 3 states x̂
+ 3 estimated states x̂
  1 measured outputs ym (1 integrating states)
  1 unmeasured outputs yu
  0 measured disturbances d
@@ -270,7 +270,7 @@ julia> model = LinModel([tf(3, [30, 1]); tf(-2, [5, 1])], 0.5);
 julia> estim = KalmanFilter(model, i_ym=[2], σR=[1], σP0=[100, 100], σQint_ym=[0.01])
 KalmanFilter estimator with a sample time Ts = 0.5 s, LinModel and:
  1 manipulated inputs u (0 integrating states)
- 3 states x̂
+ 3 estimated states x̂
  1 measured outputs ym (1 integrating states)
  1 unmeasured outputs yu
  0 measured disturbances d
@@ -431,7 +431,7 @@ julia> model = NonLinModel((x,u,_)->0.1x+u, (x,_)->2x, 10.0, 1, 1, 1);
 julia> estim = UnscentedKalmanFilter(model, σR=[1], nint_ym=[2], σP0int_ym=[1, 1])
 UnscentedKalmanFilter estimator with a sample time Ts = 10.0 s, NonLinModel and:
  1 manipulated inputs u (0 integrating states)
- 3 states x̂
+ 3 estimated states x̂
  1 measured outputs ym (2 integrating states)
  0 unmeasured outputs yu
  0 measured disturbances d
@@ -665,7 +665,7 @@ julia> model = NonLinModel((x,u,_)->0.2x+u, (x,_)->-3x, 5.0, 1, 1, 1);
 julia> estim = ExtendedKalmanFilter(model, σQ=[2], σQint_ym=[2], σP0=[0.1], σP0int_ym=[0.1])
 ExtendedKalmanFilter estimator with a sample time Ts = 5.0 s, NonLinModel and:
  1 manipulated inputs u (0 integrating states)
- 2 states x̂
+ 2 estimated states x̂
  1 measured outputs ym (1 integrating states)
  0 unmeasured outputs yu
  0 measured disturbances d

src/estimator/luenberger.jl

@@ -74,7 +74,7 @@ julia> model = LinModel([tf(3, [30, 1]); tf(-2, [5, 1])], 0.5);
 julia> estim = Luenberger(model, nint_ym=[1, 1], p̂=[0.61, 0.62, 0.63, 0.64])
 Luenberger estimator with a sample time Ts = 0.5 s, LinModel and:
  1 manipulated inputs u (0 integrating states)
- 4 states x̂
+ 4 estimated states x̂
  2 measured outputs ym (2 integrating states)
  0 unmeasured outputs yu
  0 measured disturbances d

src/estimator/mhe.jl

@@ -155,7 +155,7 @@ function print_estim_dim(io::IO, estim::MovingHorizonEstimator, n)
     He = estim.He
     println(io, "$(lpad(He, n)) estimation steps He")
     println(io, "$(lpad(nu, n)) manipulated inputs u ($(sum(estim.nint_u)) integrating states)")
-    println(io, "$(lpad(nx̂, n)) states x̂")
+    println(io, "$(lpad(nx̂, n)) estimated states x̂")
     println(io, "$(lpad(nym, n)) measured outputs ym ($(sum(estim.nint_ym)) integrating states)")
     println(io, "$(lpad(nyu, n)) unmeasured outputs yu")
     print(io,   "$(lpad(nd, n)) measured disturbances d")

src/estimator/mhe/construct.jl

@@ -50,6 +50,7 @@ the augmented process model and ``\mathbf{R̂}, \mathbf{Q̂}`` covariances. The
 # Arguments
 - `model::SimModel` : (deterministic) model for the estimations.
 - `He=nothing` : estimation horizon ``H_e``, must be specified.
+- `Cwt=1e5` : slack variable weight ``C`` (scalar), use `Cwt=Inf` for hard constraints only.
 - `optim=default_optim_mhe(model)` : a [`JuMP.Model`](https://jump.dev/JuMP.jl/stable/api/JuMP/#JuMP.Model)
    with a quadratic/nonlinear optimizer for solving (default to [`Ipopt`](https://github.com/jump-dev/Ipopt.jl),
    or [`OSQP`](https://osqp.org/docs/parsers/jump.html) if `model` is a [`LinModel`](@ref)).
@@ -64,7 +65,7 @@ julia> estim = MovingHorizonEstimator(model, He=5, σR=[1], σP0=[0.01])
 MovingHorizonEstimator estimator with a sample time Ts = 10.0 s, Ipopt optimizer, NonLinModel and:
  5 estimation steps He
  1 manipulated inputs u (0 integrating states)
- 2 states x̂
+ 2 estimated states x̂
  1 measured outputs ym (1 integrating states)
  0 unmeasured outputs yu
  0 measured disturbances d
@@ -573,7 +574,7 @@ julia> estim = setconstraint!(estim, x̂min=[-50, -50], x̂max=[50, 50])
 MovingHorizonEstimator estimator with a sample time Ts = 1.0 s, OSQP optimizer, LinModel and:
  3 estimation steps He
  1 manipulated inputs u (0 integrating states)
- 2 states x̂
+ 2 estimated states x̂
  1 measured outputs ym (1 integrating states)
  0 unmeasured outputs yu
  0 measured disturbances d

src/state_estim.jl

@@ -45,7 +45,7 @@ function print_estim_dim(io::IO, estim::StateEstimator, n)
     nu, nd = estim.model.nu, estim.model.nd
     nx̂, nym, nyu = estim.nx̂, estim.nym, estim.nyu
     println(io, "$(lpad(nu, n)) manipulated inputs u ($(sum(estim.nint_u)) integrating states)")
-    println(io, "$(lpad(nx̂, n)) states x̂")
+    println(io, "$(lpad(nx̂, n)) estimated states x̂")
     println(io, "$(lpad(nym, n)) measured outputs ym ($(sum(estim.nint_ym)) integrating states)")
     println(io, "$(lpad(nyu, n)) unmeasured outputs yu")
     print(io,   "$(lpad(nd, n)) measured disturbances d")