doc: fixing jldoctest

Author: franckgaga

src/controller/nonlinmpc.jl

@@ -229,6 +229,8 @@ NonLinMPC controller with a sample time Ts = 10.0 s:
 ├ model: NonLinModel
 ├ optimizer: Ipopt
 ├ transcription: SingleShooting
+├ gradient: AutoForwardDiff
+├ jacobian: AutoForwardDiff
 └ dimensions:
   ├ 20 prediction steps Hp
   ├  1 control steps Hc
@@ -338,6 +340,8 @@ NonLinMPC controller with a sample time Ts = 10.0 s:
 ├ model: NonLinModel
 ├ optimizer: Ipopt
 ├ transcription: SingleShooting
+├ gradient: AutoForwardDiff
+├ jacobian: AutoForwardDiff
 └ dimensions:
   ├ 20 prediction steps Hp
   ├  1 control steps Hc

src/estimator/mhe/construct.jl

@@ -278,6 +278,8 @@ julia> estim = MovingHorizonEstimator(model, He=5, σR=[1], σP_0=[0.01])
 MovingHorizonEstimator estimator with a sample time Ts = 10.0 s:
 ├ model: NonLinModel
 ├ optimizer: Ipopt
+├ gradient: AutoForwardDiff
+├ jacobian: AutoForwardDiff
 └ dimensions:
   ├ 5 estimation steps He
   ├ 0 slack variable ϵ (estimation constraints)

src/model/nonlinmodel.jl

@@ -154,6 +154,7 @@ julia> h!(y, x, _ , _ ) = (y .= 0.1x; nothing);
 julia> model1 = NonLinModel(f!, h!, 5.0, 1, 1, 1, p=-0.2)       # continuous dynamics
 NonLinModel with a sample time Ts = 5.0 s:
 ├ solver: RungeKutta(4)
+├ linearization: AutoForwardDiff
 └ dimensions:
   ├ 1 manipulated inputs u
   ├ 1 states x
@@ -166,7 +167,8 @@ julia> h(x, _ , _ ) = 2x;
 
 julia> model2 = NonLinModel(f, h, 2.0, 1, 1, 1, solver=nothing) # discrete dynamics
 NonLinModel with a sample time Ts = 2.0 s:
-├ solver: empty
+├ solver: EmptySolver
+├ linearization: AutoForwardDiff
 └ dimensions:
   ├ 1 manipulated inputs u
   ├ 1 states x