doc: minor mistake corrections

Author: franckgaga

src/estimator/kalman.jl

@@ -978,9 +978,9 @@ differentiation. This estimator is allocation-free if `model` simulations do not
 - `σR=fill(1,length(i_ym))` or *`sigmaR`* : main diagonal of the sensor noise covariance
     ``\mathbf{R}`` of `model` measured outputs, specified as a standard deviation vector.
 - `nint_u=0`: integrator quantity for the stochastic model of the unmeasured disturbances at
-    the manipulated inputs (vector), use `nint_u=0` for no integrator (see Extended Help).
+    the manipulated inputs (vector), use `nint_u=0` for no integrator.
 - `nint_ym=default_nint(model,i_ym,nint_u)` : same than `nint_u` but for the unmeasured 
-    disturbances at the measured outputs, use `nint_ym=0` for no integrator (see Extended Help).
+    disturbances at the measured outputs, use `nint_ym=0` for no integrator.
 - `σQint_u=fill(1,sum(nint_u))` or *`sigmaQint_u`* : same than `σQ` but for the unmeasured
     disturbances at manipulated inputs ``\mathbf{Q_{int_u}}`` (composed of integrators).
 - `σPint_u_0=fill(1,sum(nint_u))` or *`sigmaPint_u_0`* : same than `σP_0` but for the unmeasured

src/model/nonlinmodel.jl

@@ -90,7 +90,7 @@ functions are defined as:
 ```
 where ``\mathbf{x}``, ``\mathbf{y}``, ``\mathbf{u}``, ``\mathbf{d}`` and ``\mathbf{p}`` are
 respectively the state, output, manipulated input, measured disturbance and parameter
-vectors. As a mather of fact, the parameter argument `p` can be any Julia objects but use a
+vectors. As a matter of fact, the parameter argument `p` can be any Julia objects but use a
 mutable type if you want to change them later e.g.: a vector. If the dynamics is a function
 of the time, simply add a measured disturbance defined as ``d(t) = t``. The functions can be
 implemented in two possible ways: