diff --git a/src/controller/nonlinmpc.jl b/src/controller/nonlinmpc.jl
index a8af925d..a990c442 100644
--- a/src/controller/nonlinmpc.jl
+++ b/src/controller/nonlinmpc.jl
@@ -162,11 +162,14 @@ NonLinMPC controller with a sample time Ts = 10.0 s, Ipopt optimizer, UnscentedK
     algebra instead of a `for` loop. This feature can accelerate the optimization, especially
     for the constraint handling, and is not available in any other package, to my knowledge.
 
-    The optimization relies on [`JuMP.jl`](https://github.com/jump-dev/JuMP.jl) automatic 
+    The optimization relies on [`JuMP`](https://github.com/jump-dev/JuMP.jl) automatic 
     differentiation (AD) to compute the objective and constraint derivatives. Optimizers 
     generally benefit from exact derivatives like AD. However, the [`NonLinModel`](@ref) `f` 
     and `h` functions must be compatible with this feature. See [Automatic differentiation](https://jump.dev/JuMP.jl/stable/manual/nlp/#Automatic-differentiation)
     for common mistakes when writing these functions.
+
+    Note that if `Cwt≠Inf`, the attribute `nlp_scaling_max_gradient` of `Ipopt` is set to 
+    `10/Cwt` (if not already set), to scale the small values of ``ϵ``.
 """
 function NonLinMPC(
     model::SimModel;
diff --git a/src/estimator/mhe/construct.jl b/src/estimator/mhe/construct.jl
index a931e483..5d0f0b14 100644
--- a/src/estimator/mhe/construct.jl
+++ b/src/estimator/mhe/construct.jl
@@ -250,11 +250,15 @@ MovingHorizonEstimator estimator with a sample time Ts = 10.0 s, Ipopt optimizer
     state and sensor noise).
 
     For [`LinModel`](@ref), the optimization is treated as a quadratic program with a
-    time-varying Hessian, which is generally cheaper than nonlinear programming. For 
-    [`NonLinModel`](@ref), the optimization relies on automatic differentiation (AD).
+    time-varying Hessian, which is generally cheaper than nonlinear programming.
+    
+    For [`NonLinModel`](@ref), the optimization relies on automatic differentiation (AD).
     Optimizers generally benefit from exact derivatives like AD. However, the `f` and `h` 
     functions must be compatible with this feature. See [Automatic differentiation](https://jump.dev/JuMP.jl/stable/manual/nlp/#Automatic-differentiation)
-    for common mistakes when writing these functions.
+    for common mistakes when writing these functions. 
+        
+    Note that if `Cwt≠Inf`, the attribute `nlp_scaling_max_gradient` of `Ipopt` is set to 
+    `10/Cwt` (if not already set), to scale the small values of ``ϵ``.
 """
 function MovingHorizonEstimator(
     model::SM;
diff --git a/src/estimator/mhe/execute.jl b/src/estimator/mhe/execute.jl
index 87c59294..0a6b4277 100644
--- a/src/estimator/mhe/execute.jl
+++ b/src/estimator/mhe/execute.jl
@@ -192,9 +192,10 @@ also inits `estim.optim` objective function, expressed as the quadratic general
 ```math
     J = \min_{\mathbf{Z̃}} \frac{1}{2}\mathbf{Z̃' H̃ Z̃} + \mathbf{q̃' Z̃} + p 
 ```
-in which ``\mathbf{Z̃} = [\begin{smallmatrix} ϵ \\ \mathbf{Z} \end{smallmatrix}]``. The
+in which ``\mathbf{Z̃} = [\begin{smallmatrix} ϵ \\ \mathbf{Z} \end{smallmatrix}]``. Note that
+``p`` is useless at optimization but required to evaluate the objective minima ``J``. The 
 Hessian ``\mathbf{H̃}`` matrix of the quadratic general form is not constant here because
-of the time-varying ``\mathbf{P̄}`` covariance . The computations are:
+of the time-varying ``\mathbf{P̄}`` covariance . The computed variables are:
 ```math
 \begin{aligned}
     \mathbf{F}       &= \mathbf{G U} + \mathbf{J D} + \mathbf{Y^m} \\