minor doc correction

Author: franckgaga

docs/src/manual/nonlinmpc.md

@@ -35,8 +35,9 @@ The plant model is nonlinear:
 
 in which ``g`` is the gravitational acceleration in m/s², ``L``, the pendulum length in m,
 ``K``, the friction coefficient at the pivot point in kg/s, and ``m``, the mass attached at
-the end of the pendulum in kg. Here, a [fourth order Runge-Kutta method](https://en.wikipedia.org/wiki/Runge%E2%80%93Kutta_methods)
-solves the differential equations to construct a [`NonLinModel`](@ref):
+the end of the pendulum in kg. The [`NonLinModel`](@ref) constructor assumes by default
+that the state function `f` is continuous in time, that is, an ordinary differential
+equation system (like here):
 
 ```@example 1
 using ModelPredictiveControl

src/estimator/mhe/construct.jl

@@ -187,7 +187,7 @@ N_k =                     \begin{cases}
 The vectors ``\mathbf{Ŵ}`` and ``\mathbf{V̂}`` encompass the estimated process noise
 ``\mathbf{ŵ}(k-j)`` and sensor noise ``\mathbf{v̂}(k-j)`` from ``j=N_k-1`` to ``0``. The 
 Extended Help defines the two vectors and the scalar ``ϵ``. See [`UnscentedKalmanFilter`](@ref)
-for details on he augmented process model and ``\mathbf{R̂}, \mathbf{Q̂}`` covariances. The
+for details on the augmented process model and ``\mathbf{R̂}, \mathbf{Q̂}`` covariances. The
 matrix ``\mathbf{P̂}_{k-N_k}(k-N_k+1)`` is estimated with an [`ExtendedKalmanFilter`](@ref).
 
 !!! warning