Merge pull request #49 from JuliaControl/print_slack_var

added: print info on controller and estimator constraints softening

Author: franckgaga

docs/src/manual/nonlinmpc.md

@@ -112,8 +112,7 @@ nmpc = NonLinMPC(estim, Hp=20, Hc=2, Mwt=[0.5], Nwt=[2.5], Cwt=Inf)
 nmpc = setconstraint!(nmpc, umin=[-1.5], umax=[+1.5])
 ```
 
-The option `Cwt=Inf` disables constraint softening. We test `mpc` performance on `plant` by
-imposing an angular setpoint of 180° (inverted position):
+The option `Cwt=Inf` disables the slack variable `ϵ` for constraint softening. We test `mpc` performance on `plant` by imposing an angular setpoint of 180° (inverted position):
 
 ```@example 1
 using Logging; disable_logging(Warn)            # hide

src/controller/construct.jl

@@ -72,6 +72,7 @@ julia> mpc = setconstraint!(mpc, umin=[0], umax=[100], Δumin=[-10], Δumax=[+10
 LinMPC controller with a sample time Ts = 4.0 s, OSQP optimizer, 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)

src/controller/explicitmpc.jl

@@ -133,22 +133,7 @@ end
 
 Use custom state estimator `estim` to construct `ExplicitMPC`.
 
-`estim.model` must be a [`LinModel`](@ref). Else, a [`NonLinMPC`](@ref) is required. 
-
-# Examples
-```jldoctest
-julia> estim = KalmanFilter(LinModel([tf(3, [30, 1]); tf(-2, [5, 1])], 4), i_ym=[2]);
-
-julia> mpc = ExplicitMPC(estim, Mwt=[0, 1], Nwt=[0.5], Hp=30, Hc=1)
-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 estimated states x̂
-  1 measured outputs ym (1 integrating states)
-  1 unmeasured outputs yu
-  0 measured disturbances d
-```
+`estim.model` must be a [`LinModel`](@ref). Else, a [`NonLinMPC`](@ref) is required.
 """
 function ExplicitMPC(
     estim::SE;

src/controller/linmpc.jl

@@ -139,6 +139,7 @@ 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, 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)
@@ -203,6 +204,7 @@ 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, 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)

src/controller/nonlinmpc.jl

@@ -151,6 +151,7 @@ julia> mpc = NonLinMPC(model, Hp=20, Hc=1, Cwt=1e6)
 NonLinMPC controller with a sample time Ts = 10.0 s, Ipopt optimizer, 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)
@@ -229,6 +230,7 @@ julia> mpc = NonLinMPC(estim, Hp=20, Hc=1, Cwt=1e6)
 NonLinMPC controller with a sample time Ts = 10.0 s, Ipopt optimizer, 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)

src/estimator/mhe.jl

@@ -16,7 +16,9 @@ function print_estim_dim(io::IO, estim::MovingHorizonEstimator, n)
     nu, nd = estim.model.nu, estim.model.nd
     nx̂, nym, nyu = estim.nx̂, estim.nym, estim.nyu
     He = estim.He
+    nϵ = isinf(estim.C) ? 0 : 1
     println(io, "$(lpad(He, n)) estimation steps He")
+    println(io, "$(lpad(nϵ, n)) slack variable ϵ (estimation constraints)")
     println(io, "$(lpad(nu, n)) manipulated inputs u ($(sum(estim.nint_u)) integrating states)")
     println(io, "$(lpad(nx̂, n)) estimated states x̂")
     println(io, "$(lpad(nym, n)) measured outputs ym ($(sum(estim.nint_ym)) integrating states)")

src/estimator/mhe/construct.jl

@@ -212,6 +212,7 @@ julia> model = NonLinModel((x,u,_)->0.1x+u, (x,_)->2x, 10.0, 1, 1, 1, solver=not
 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
+ 0 slack variable ϵ (estimation constraints)
  1 manipulated inputs u (0 integrating states)
  2 estimated states x̂
  1 measured outputs ym (1 integrating states)
@@ -366,6 +367,7 @@ 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)

src/predictive_control.jl

@@ -34,6 +34,7 @@ setstate!(mpc::PredictiveController, x̂) = (setstate!(mpc.estim, x̂); return m
 
 function Base.show(io::IO, mpc::PredictiveController)
     Hp, Hc = mpc.Hp, mpc.Hc
+    nϵ = isinf(mpc.C) ? 0 : 1
     nu, nd = mpc.estim.model.nu, mpc.estim.model.nd
     nx̂, nym, nyu = mpc.estim.nx̂, mpc.estim.nym, mpc.estim.nyu
     n = maximum(ndigits.((Hp, Hc, nu, nx̂, nym, nyu, nd))) + 1
@@ -42,6 +43,7 @@ function Base.show(io::IO, mpc::PredictiveController)
                 "$(typeof(mpc.estim).name.name) estimator and:")
     println(io, "$(lpad(Hp, n)) prediction steps Hp")
     println(io, "$(lpad(Hc, n)) control steps Hc")
+    println(io, "$(lpad(nϵ, n)) slack variable ϵ (control constraints)")
     print_estim_dim(io, mpc.estim, n)
 end