Merge pull request #244 from JuliaControl/prettier_print

added: clearer pretty-print with trees + print differentiation backends

Author: franckgaga

Project.toml

@@ -1,7 +1,7 @@
 name = "ModelPredictiveControl"
 uuid = "61f9bdb8-6ae4-484a-811f-bbf86720c31c"
 authors = ["Francis Gagnon"]
-version = "1.9.1"
+version = "1.9.2"
 
 [deps]
 ControlSystemsBase = "aaaaaaaa-a6ca-5380-bf3e-84a91bcd477e"

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
 ```
 
 """
@@ -177,15 +178,15 @@ end
 setconstraint!(::ExplicitMPC; kwargs...) = error("ExplicitMPC does not support constraints.")
 
 function Base.show(io::IO, mpc::ExplicitMPC)
-    Hp, Hc = mpc.Hp, mpc.Hc
-    nu, nd = mpc.estim.model.nu, mpc.estim.model.nd
-    nx̂, nym, nyu = mpc.estim.nx̂, mpc.estim.nym, mpc.estim.nyu
+    estim, model = mpc.estim, mpc.estim.model
+    Hp, Hc, nϵ = mpc.Hp, mpc.Hc, mpc.nϵ
+    nu, nd = model.nu, model.nd
+    nx̂, nym, nyu = estim.nx̂, estim.nym, estim.nyu
     n = maximum(ndigits.((Hp, Hc, nu, nx̂, nym, nyu, nd))) + 1
-    println(io, "$(nameof(typeof(mpc))) controller with a sample time Ts = "*
-                "$(mpc.estim.model.Ts) s, "*
-                "$(nameof(typeof(mpc.estim))) estimator and:")
-    println(io, "$(lpad(Hp, n)) prediction steps Hp")
-    println(io, "$(lpad(Hc, n)) control steps Hc")
+    println(io, "$(nameof(typeof(mpc))) controller with a sample time Ts = $(model.Ts) s:")
+    println(io, "├ estimator: $(nameof(typeof(mpc.estim)))")
+    println(io, "├ model: $(nameof(typeof(model)))")
+    println(io, "└ dimensions:")
     print_estim_dim(io, mpc.estim, n)
 end
 

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

@@ -223,16 +223,23 @@ This controller allocates memory at each time step for the optimization.
 ```jldoctest
 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
+julia> mpc = NonLinMPC(model, Hp=20, Hc=10, transcription=MultipleShooting())
+NonLinMPC controller with a sample time Ts = 10.0 s:
+├ estimator: UnscentedKalmanFilter
+├ model: NonLinModel
+├ optimizer: Ipopt 
+├ transcription: MultipleShooting
+├ gradient: AutoForwardDiff
+├ jacobian: AutoSparse (AutoForwardDiff, TracerSparsityDetector, GreedyColoringAlgorithm)
+└ dimensions:
+  ├ 20 prediction steps Hp
+  ├ 10 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
@@ -327,16 +334,23 @@ 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
+julia> mpc = NonLinMPC(estim, Hp=20, Cwt=1e6)
+NonLinMPC controller with a sample time Ts = 10.0 s:
+├ estimator: UnscentedKalmanFilter
+├ model: NonLinModel
+├ optimizer: Ipopt
+├ transcription: SingleShooting
+├ gradient: AutoForwardDiff
+├ jacobian: AutoForwardDiff
+└ dimensions:
+  ├ 20 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
 ```
 """
 function NonLinMPC(
@@ -738,8 +752,14 @@ end
 
 "Evaluate the economic term `E*JE` of the objective function for [`NonLinMPC`](@ref)."
 function obj_econ(
-    mpc::NonLinMPC, model::SimModel, Ue, Ŷe::AbstractVector{NT}
+    mpc::NonLinMPC, ::SimModel, Ue, Ŷe::AbstractVector{NT}
 ) where NT<:Real
     E_JE = mpc.weights.iszero_E ? zero(NT) : mpc.weights.E*mpc.JE(Ue, Ŷe, mpc.D̂e, mpc.p)
     return E_JE
-end
+end
+
+"Print the differentiation backends of a [`NonLinMPC`](@ref) controller."
+function print_backends(io::IO, mpc::NonLinMPC)
+    println(io, "├ gradient: $(backend_str(mpc.gradient))")
+    println(io, "├ jacobian: $(backend_str(mpc.jacobian))")
+end

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
@@ -355,10 +357,10 @@ end
 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)) estimated states x̂")
-    println(io, "$(lpad(nym, n)) measured outputs ym")
-    println(io, "$(lpad(nyu, n)) unmeasured outputs yu")
-    print(io,   "$(lpad(nd, n)) measured disturbances d")
+    println(io, "  ├$(lpad(nu, n)) manipulated inputs u")
+    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")
 end
 

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 +1008,15 @@ 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
+├ jacobian: AutoForwardDiff
+└ 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(
@@ -1169,6 +1178,10 @@ function update_estimate!(estim::ExtendedKalmanFilter{NT}, y0m, d0, u0) where NT
     return predict_estimate_kf!(estim, u0, d0, F̂)
 end
 
+function print_details(io::IO, estim::ExtendedKalmanFilter)
+    println(io, "├ jacobian: $(backend_str(estim.jacobian))")
+end
+
 "Set `estim.cov.P̂` to `estim.cov.P̂_0` for the time-varying Kalman Filters."
 function init_estimate_cov!(
     estim::Union{KalmanFilter, UnscentedKalmanFilter, 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(