added: show TranscriptionMethod when printing PredictiveController

docs/src/manual/linmpc.md

@@ -78,8 +78,9 @@ mpc = setconstraint!(mpc, ymin=[48, -Inf])
 
 in which `Hp` and `Hc` keyword arguments are respectively the predictive and control
 horizons, and `Mwt` and `Nwt`, the output setpoint tracking and move suppression weights. By
-default, [`LinMPC`](@ref) controllers use [`OSQP`](https://osqp.org/) to solve the problem,
-soft constraints on output predictions ``\mathbf{ŷ}`` to ensure feasibility, and a
+default, [`LinMPC`](@ref) controllers use [`OSQP`](https://osqp.org/) and a direct
+[`SingleShooting`](@ref) transcription method to solve the optimal control problem, soft
+constraints on output predictions ``\mathbf{ŷ}`` to ensure feasibility, and a
 [`SteadyKalmanFilter`](@ref) to estimate the plant states[^1]. An attentive reader will also
 notice that the Kalman filter estimates two additional states compared to the plant model.
 These are the integrating states for the unmeasured plant disturbances, and they are

docs/src/manual/nonlinmpc.md

@@ -119,8 +119,10 @@ umin, umax = [-1.5], [+1.5]
 nmpc = setconstraint!(nmpc; umin, umax)
 ```
 
-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):
+The option `Cwt=Inf` disables the slack variable `ϵ` for constraint softening. By default,
+[`NonLinMPC`](@ref) controllers use [`Ipopt`](https://coin-or.github.io/Ipopt/) and a direct
+[`SingleShooting`](@ref) transcription method to solve the optimal control problem. We test
+`mpc` performance on `plant` by imposing an angular setpoint of 180° (inverted position):
 
 ```@example man_nonlin
 using JuMP; unset_time_limit_sec(nmpc.optim) # hide

src/controller/construct.jl

@@ -221,7 +221,7 @@ 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, SteadyKalmanFilter estimator and:
+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)

src/controller/linmpc.jl

@@ -167,7 +167,7 @@ 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, SteadyKalmanFilter estimator and:
+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)
@@ -237,7 +237,7 @@ 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, KalmanFilter estimator and:
+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)

src/controller/nonlinmpc.jl

@@ -223,7 +223,7 @@ 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, UnscentedKalmanFilter estimator and:
+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)
@@ -327,7 +327,7 @@ 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, UnscentedKalmanFilter estimator and:
+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)

src/predictive_control.jl

@@ -33,6 +33,7 @@ function Base.show(io::IO, mpc::PredictiveController)
     n = maximum(ndigits.((Hp, Hc, nu, nx̂, nym, nyu, nd))) + 1
     println(io, "$(typeof(mpc).name.name) controller with a sample time Ts = "*
                 "$(mpc.estim.model.Ts) s, $(JuMP.solver_name(mpc.optim)) optimizer, "*
+                "$(typeof(mpc.transcription).name.name) transcription, "*
                 "$(typeof(mpc.estim).name.name) estimator and:")
     println(io, "$(lpad(Hp, n)) prediction steps Hp")
     println(io, "$(lpad(Hc, n)) control steps Hc")