doc: jldoctest modifications

Author: franckgaga

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)