doc: update jldoctests

Author: franckgaga

src/controller/construct.jl

@@ -221,15 +221,20 @@ constraints are all soft by default. See Extended Help for time-varying constrai
 julia> mpc = LinMPC(setop!(LinModel(tf(3, [30, 1]), 4), uop=[50], yop=[25]));
 
 julia> mpc = setconstraint!(mpc, umin=[0], umax=[100], Δumin=[-10], Δumax=[+10])
-LinMPC controller with a sample time Ts = 4.0 s, OSQP optimizer, SingleShooting transcription, SteadyKalmanFilter estimator and:
- 10 prediction steps Hp
-  2 control steps Hc
-  1 slack variable ϵ (control constraints)
-  1 manipulated inputs u (0 integrating states)
-  2 estimated states x̂
-  1 measured outputs ym (1 integrating states)
-  0 unmeasured outputs yu
-  0 measured disturbances d
+LinMPC controller with a sample time Ts = 4.0 s:
+├ estimator: SteadyKalmanFilter
+├ model: LinModel
+├ optimizer: OSQP
+├ transcription: SingleShooting
+└ dimensions:
+  ├ 10 prediction steps Hp
+  ├  2 control steps Hc
+  ├  1 slack variable ϵ (control constraints)
+  ├  1 manipulated inputs u (0 integrating states)
+  ├  2 estimated states x̂
+  ├  1 measured outputs ym (1 integrating states)
+  ├  0 unmeasured outputs yu
+  └  0 measured disturbances d
 ```
 
 # Extended Help

src/controller/explicitmpc.jl

@@ -117,14 +117,15 @@ arguments.
 julia> model = LinModel([tf(3, [30, 1]); tf(-2, [5, 1])], 4);
 
 julia> mpc = ExplicitMPC(model, Mwt=[0, 1], Nwt=[0.5], Hp=30, Hc=1)
-ExplicitMPC controller with a sample time Ts = 4.0 s, SteadyKalmanFilter estimator and:
- 30 prediction steps Hp
-  1 control steps Hc
-  1 manipulated inputs u (0 integrating states)
-  4 estimated states x̂
-  2 measured outputs ym (2 integrating states)
-  0 unmeasured outputs yu
-  0 measured disturbances d
+ExplicitMPC controller with a sample time Ts = 4.0 s:
+├ estimator: SteadyKalmanFilter
+├ model: LinModel
+└ dimensions:
+  ├  1 manipulated inputs u (0 integrating states)
+  ├  4 estimated states x̂
+  ├  2 measured outputs ym (2 integrating states)
+  ├  0 unmeasured outputs yu
+  └  0 measured disturbances d
 ```
 
 """

src/controller/linmpc.jl

@@ -168,15 +168,20 @@ arguments. This controller allocates memory at each time step for the optimizati
 julia> model = LinModel([tf(3, [30, 1]); tf(-2, [5, 1])], 4);
 
 julia> mpc = LinMPC(model, Mwt=[0, 1], Nwt=[0.5], Hp=30, Hc=1)
-LinMPC controller with a sample time Ts = 4.0 s, OSQP optimizer, SingleShooting transcription, SteadyKalmanFilter estimator and:
- 30 prediction steps Hp
-  1 control steps Hc
-  1 slack variable ϵ (control constraints)
-  1 manipulated inputs u (0 integrating states)
-  4 estimated states x̂
-  2 measured outputs ym (2 integrating states)
-  0 unmeasured outputs yu
-  0 measured disturbances d
+LinMPC controller with a sample time Ts = 4.0 s:
+├ estimator: SteadyKalmanFilter
+├ model: LinModel
+├ optimizer: OSQP
+├ transcription: SingleShooting
+└ dimensions:
+  ├ 30 prediction steps Hp
+  ├  1 control steps Hc
+  ├  1 slack variable ϵ (control constraints)
+  ├  1 manipulated inputs u (0 integrating states)
+  ├  4 estimated states x̂
+  ├  2 measured outputs ym (2 integrating states)
+  ├  0 unmeasured outputs yu
+  └  0 measured disturbances d
 ```
 
 # Extended Help
@@ -238,15 +243,20 @@ Use custom state estimator `estim` to construct `LinMPC`.
 julia> estim = KalmanFilter(LinModel([tf(3, [30, 1]); tf(-2, [5, 1])], 4), i_ym=[2]);
 
 julia> mpc = LinMPC(estim, Mwt=[0, 1], Nwt=[0.5], Hp=30, Hc=1)
-LinMPC controller with a sample time Ts = 4.0 s, OSQP optimizer, SingleShooting transcription, KalmanFilter estimator and:
- 30 prediction steps Hp
-  1 control steps Hc
-  1 slack variable ϵ (control constraints)
-  1 manipulated inputs u (0 integrating states)
-  3 estimated states x̂
-  1 measured outputs ym (1 integrating states)
-  1 unmeasured outputs yu
-  0 measured disturbances d
+LinMPC controller with a sample time Ts = 4.0 s:
+├ estimator: KalmanFilter
+├ model: LinModel
+├ optimizer: OSQP
+├ transcription: SingleShooting
+└ dimensions:
+  ├ 30 prediction steps Hp
+  ├  1 control steps Hc
+  ├  1 slack variable ϵ (control constraints)
+  ├  1 manipulated inputs u (0 integrating states)
+  ├  3 estimated states x̂
+  ├  1 measured outputs ym (1 integrating states)
+  ├  1 unmeasured outputs yu
+  └  0 measured disturbances d
 ```
 """
 function LinMPC(

src/controller/nonlinmpc.jl

@@ -224,15 +224,20 @@ This controller allocates memory at each time step for the optimization.
 julia> model = NonLinModel((x,u,_,_)->0.5x+u, (x,_,_)->2x, 10.0, 1, 1, 1, solver=nothing);
 
 julia> mpc = NonLinMPC(model, Hp=20, Hc=1, Cwt=1e6)
-NonLinMPC controller with a sample time Ts = 10.0 s, Ipopt optimizer, SingleShooting transcription, UnscentedKalmanFilter estimator and:
- 20 prediction steps Hp
-  1 control steps Hc
-  1 slack variable ϵ (control constraints)
-  1 manipulated inputs u (0 integrating states)
-  2 estimated states x̂
-  1 measured outputs ym (1 integrating states)
-  0 unmeasured outputs yu
-  0 measured disturbances d
+NonLinMPC controller with a sample time Ts = 10.0 s:
+├ estimator: UnscentedKalmanFilter
+├ model: NonLinModel
+├ optimizer: Ipopt
+├ transcription: SingleShooting
+└ dimensions:
+  ├ 20 prediction steps Hp
+  ├  1 control steps Hc
+  ├  1 slack variable ϵ (control constraints)
+  ├  1 manipulated inputs u (0 integrating states)
+  ├  2 estimated states x̂
+  ├  1 measured outputs ym (1 integrating states)
+  ├  0 unmeasured outputs yu
+  └  0 measured disturbances d
 ```
 
 # Extended Help
@@ -328,15 +333,20 @@ julia> model = NonLinModel((x,u,_,_)->0.5x+u, (x,_,_)->2x, 10.0, 1, 1, 1, solver
 julia> estim = UnscentedKalmanFilter(model, σQint_ym=[0.05]);
 
 julia> mpc = NonLinMPC(estim, Hp=20, Hc=1, Cwt=1e6)
-NonLinMPC controller with a sample time Ts = 10.0 s, Ipopt optimizer, SingleShooting transcription, UnscentedKalmanFilter estimator and:
- 20 prediction steps Hp
-  1 control steps Hc
-  1 slack variable ϵ (control constraints)
-  1 manipulated inputs u (0 integrating states)
-  2 estimated states x̂
-  1 measured outputs ym (1 integrating states)
-  0 unmeasured outputs yu
-  0 measured disturbances d
+NonLinMPC controller with a sample time Ts = 10.0 s:
+├ estimator: UnscentedKalmanFilter
+├ model: NonLinModel
+├ optimizer: Ipopt
+├ transcription: SingleShooting
+└ dimensions:
+  ├ 20 prediction steps Hp
+  ├  1 control steps Hc
+  ├  1 slack variable ϵ (control constraints)
+  ├  1 manipulated inputs u (0 integrating states)
+  ├  2 estimated states x̂
+  ├  1 measured outputs ym (1 integrating states)
+  ├  0 unmeasured outputs yu
+  └  0 measured disturbances d
 ```
 """
 function NonLinMPC(

src/estimator/internal_model.jl

@@ -80,12 +80,14 @@ estimator is allocation-free if `model` simulations do not allocate.
 # Examples
 ```jldoctest
 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 estimated states x̂
- 1 measured outputs ym
- 1 unmeasured outputs yu
- 0 measured disturbances d
+InternalModel estimator with a sample time Ts = 0.5 s:
+├ model: LinModel
+└ dimensions:
+  ├ 1 manipulated inputs u
+  ├ 2 estimated states x̂
+  ├ 1 measured outputs ym
+  ├ 1 unmeasured outputs yu
+  └ 0 measured disturbances d
 ```
 
 # Extended Help

src/estimator/kalman.jl

@@ -130,12 +130,14 @@ state of the next time step ``\mathbf{x̂}_k(k+1)``. This estimator is allocatio
 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 estimated states x̂
- 1 measured outputs ym (1 integrating states)
- 1 unmeasured outputs yu
- 0 measured disturbances d
+SteadyKalmanFilter estimator with a sample time Ts = 0.5 s:
+├ model: LinModel
+└ dimensions:
+  ├ 1 manipulated inputs u (0 integrating states)
+  ├ 3 estimated states x̂
+  ├ 1 measured outputs ym (1 integrating states)
+  ├ 1 unmeasured outputs yu
+  └ 0 measured disturbances d
 ```
 
 # Extended Help
@@ -395,12 +397,14 @@ This estimator is allocation-free.
 julia> model = LinModel([tf(3, [30, 1]); tf(-2, [5, 1])], 0.5);
 
 julia> estim = KalmanFilter(model, i_ym=[2], σR=[1], σP_0=[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 estimated states x̂
- 1 measured outputs ym (1 integrating states)
- 1 unmeasured outputs yu
- 0 measured disturbances d
+KalmanFilter estimator with a sample time Ts = 0.5 s:
+├ model: LinModel
+└ dimensions:
+  ├ 1 manipulated inputs u (0 integrating states)
+  ├ 3 estimated states x̂
+  ├ 1 measured outputs ym (1 integrating states)
+  ├ 1 unmeasured outputs yu
+  └ 0 measured disturbances d
 ```
 """
 function KalmanFilter(
@@ -639,12 +643,14 @@ This estimator is allocation-free if `model` simulations do not allocate.
 julia> model = NonLinModel((x,u,_,_)->0.1x+u, (x,_,_)->2x, 10.0, 1, 1, 1, solver=nothing);
 
 julia> estim = UnscentedKalmanFilter(model, σR=[1], nint_ym=[2], σPint_ym_0=[1, 1])
-UnscentedKalmanFilter estimator with a sample time Ts = 10.0 s, NonLinModel and:
- 1 manipulated inputs u (0 integrating states)
- 3 estimated states x̂
- 1 measured outputs ym (2 integrating states)
- 0 unmeasured outputs yu
- 0 measured disturbances d
+UnscentedKalmanFilter estimator with a sample time Ts = 10.0 s:
+├ model: NonLinModel
+└ dimensions:
+  ├ 1 manipulated inputs u (0 integrating states)
+  ├ 3 estimated states x̂
+  ├ 1 measured outputs ym (2 integrating states)
+  ├ 0 unmeasured outputs yu
+  └ 0 measured disturbances d
 ```
 
 # Extended Help
@@ -1002,12 +1008,14 @@ differentiation. This estimator is allocation-free if `model` simulations do not
 julia> model = NonLinModel((x,u,_,_)->0.2x+u, (x,_,_)->-3x, 5.0, 1, 1, 1, solver=nothing);
 
 julia> estim = ExtendedKalmanFilter(model, σQ=[2], σQint_ym=[2], σP_0=[0.1], σPint_ym_0=[0.1])
-ExtendedKalmanFilter estimator with a sample time Ts = 5.0 s, NonLinModel and:
- 1 manipulated inputs u (0 integrating states)
- 2 estimated states x̂
- 1 measured outputs ym (1 integrating states)
- 0 unmeasured outputs yu
- 0 measured disturbances d
+ExtendedKalmanFilter estimator with a sample time Ts = 5.0 s:
+├ model: NonLinModel
+└ dimensions:
+  ├ 1 manipulated inputs u (0 integrating states)
+  ├ 2 estimated states x̂
+  ├ 1 measured outputs ym (1 integrating states)
+  ├ 0 unmeasured outputs yu
+  └ 0 measured disturbances d
 ```
 """
 function ExtendedKalmanFilter(

src/estimator/luenberger.jl

@@ -83,12 +83,14 @@ is allocation-free.
 julia> model = LinModel([tf(3, [30, 1]); tf(-2, [5, 1])], 0.5);
 
 julia> estim = Luenberger(model, nint_ym=[1, 1], poles=[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 estimated states x̂
- 2 measured outputs ym (2 integrating states)
- 0 unmeasured outputs yu
- 0 measured disturbances d
+Luenberger estimator with a sample time Ts = 0.5 s:
+├ model: LinModel
+└ dimensions:
+  ├ 1 manipulated inputs u (0 integrating states)
+  ├ 4 estimated states x̂
+  ├ 2 measured outputs ym (2 integrating states)
+  ├ 0 unmeasured outputs yu
+  └ 0 measured disturbances d
 ```
 """
 function Luenberger(

src/estimator/manual.jl

@@ -77,12 +77,14 @@ examples.
 julia> model = LinModel([tf(3, [30, 1]); tf(-2, [5, 1])], 0.5);
 
 julia> estim = ManualEstimator(model, nint_ym=0) # disable augmentation with integrators
-ManualEstimator estimator with a sample time Ts = 0.5 s, LinModel and:
- 1 manipulated inputs u (0 integrating states)
- 2 estimated states x̂
- 2 measured outputs ym (0 integrating states)
- 0 unmeasured outputs yu
- 0 measured disturbances d
+ManualEstimator estimator with a sample time Ts = 0.5 s:
+├ model: LinModel
+└ dimensions:
+  ├ 1 manipulated inputs u (0 integrating states)
+  ├ 2 estimated states x̂
+  ├ 2 measured outputs ym (0 integrating states)
+  ├ 0 unmeasured outputs yu
+  └ 0 measured disturbances d
 ```
 
 # Extended Help

src/estimator/mhe/construct.jl

@@ -275,14 +275,17 @@ transcription for now.
 julia> model = NonLinModel((x,u,_,_)->0.1x+u, (x,_,_)->2x, 10.0, 1, 1, 1, solver=nothing);
 
 julia> estim = MovingHorizonEstimator(model, He=5, σR=[1], σP_0=[0.01])
-MovingHorizonEstimator estimator with a sample time Ts = 10.0 s, Ipopt optimizer, NonLinModel and:
- 5 estimation steps He
- 0 slack variable ϵ (estimation constraints)
- 1 manipulated inputs u (0 integrating states)
- 2 estimated states x̂
- 1 measured outputs ym (1 integrating states)
- 0 unmeasured outputs yu
- 0 measured disturbances d
+MovingHorizonEstimator estimator with a sample time Ts = 10.0 s:
+├ model: NonLinModel
+├ optimizer: Ipopt
+└ dimensions:
+  ├ 5 estimation steps He
+  ├ 0 slack variable ϵ (estimation constraints)
+  ├ 1 manipulated inputs u (0 integrating states)
+  ├ 2 estimated states x̂
+  ├ 1 measured outputs ym (1 integrating states)
+  ├ 0 unmeasured outputs yu
+  └ 0 measured disturbances d
 ```
 
 # Extended Help
@@ -490,14 +493,17 @@ the constant ``p``, on model augmentation and on time-varying constraints.
 julia> estim = MovingHorizonEstimator(LinModel(ss(0.5,1,1,0,1)), He=3);
 
 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
- 0 slack variable ϵ (estimation constraints)
- 1 manipulated inputs u (0 integrating states)
- 2 estimated states x̂
- 1 measured outputs ym (1 integrating states)
- 0 unmeasured outputs yu
- 0 measured disturbances d
+MovingHorizonEstimator estimator with a sample time Ts = 1.0 s:
+├ model: LinModel
+├ optimizer: OSQP
+└ dimensions:
+  ├ 3 estimation steps He
+  ├ 0 slack variable ϵ (estimation constraints)
+  ├ 1 manipulated inputs u (0 integrating states)
+  ├ 2 estimated states x̂
+  ├ 1 measured outputs ym (1 integrating states)
+  ├ 0 unmeasured outputs yu
+  └ 0 measured disturbances d
 ```
 
 # Extended Help