Merge pull request #362 from control-toolbox/doc

Doc

Author: ocots

docs/src/api-ctbase.md

@@ -22,8 +22,8 @@ You may find in this package:
 - [Differential geometry](@ref api-ctbase-differential-geometry): tools to compute [Lie brackets](https://en.wikipedia.org/w/index.php?title=Lie_bracket_of_vector_fields&oldid=1163591634), [Poisson brackets](https://en.wikipedia.org/w/index.php?title=Poisson_manifold&oldid=1163991099#Formal_definition)...
 - [Exceptions](@ref api-ctbase-exceptions).
 - [Initial guess](@ref api-ctbase-init): tools to define an initial guess.
-- [Model](@ref api-ctbase-model): tools to modelise an optimal control problem in a functional manner.
-- [Parser](@ref api-ctbase-parser): tools to modelise an optimal control problem in an abstract manner.
+- [Model](@ref api-ctbase-model): tools to model an optimal control problem in a functional manner.
+- [Parser](@ref api-ctbase-parser): tools to model an optimal control problem in an abstract manner.
 - [Print](@ref api-ctbase-print): check how an optimal control problem is displayed.
 - [Repl](@ref api-ctbase-repl): control-toolbox repl.
 - [Solution](@ref api-ctbase-solution): optimal control solution.

docs/src/index.md

@@ -46,7 +46,7 @@ and other constraints such as
 
 ## Basic usage
 
-Let us modelise, solve and plot a simple optimal control problem.
+Let us model, solve and plot a simple optimal control problem.
 
 ```julia
 using OptimalControl
@@ -102,3 +102,7 @@ If it is your first contribution, you can also check [this first contribution tu
 You can find first good issues (if any 🙂) [here](https://github.com/control-toolbox/OptimalControl.jl/contribute). You may find other packages to contribute to at the [control-toolbox organization](https://github.com/control-toolbox).
 
 If you want to ask a question, feel free to start a discussion [here](https://github.com/orgs/control-toolbox/discussions). This forum is for general discussion about this repository and the [control-toolbox organization](https://github.com/control-toolbox).
+
+!!! note
+
+    If you want to add an application or a package to the control-toolbox ecosystem, please follow this [set up tutorial](https://github.com/control-toolbox/CTApp.jl/discussions/9).

docs/src/tutorial-solve.md

@@ -63,7 +63,7 @@ In a direct method, the optimal control problem is transcribed to a nonlinear op
 \text{minimize}\quad F(y), \quad\text{subject to the constraints}\quad g(y)=0, \quad h(y)\le 0. 
 ```
 
-OptimalControl.jl package makes the transcription but it needs a package to modelise the NLP problem and 
+OptimalControl.jl package makes the transcription but it needs a package to model the NLP problem and 
 another one to solve it. NLPModelsIpopt.jl package provides an interface to the well-known solver 
 [Ipopt](https://coin-or.github.io/Ipopt/) that can be used to solve general nonlinear programming problems.
 
@@ -96,7 +96,7 @@ solve(ocp, :direct, :adnlp, :ipopt)
 ```
 
 Let us detail the three symbols. As you can see, there are only direct methods. The symbol `:adnlp` is for the 
-choice of modeler. As said before, the NLP problem needs to be modelised in Julia code. We use 
+choice of modeler. As said before, the NLP problem needs to be modelled in Julia code. We use 
 [ADNLPModels.jl](https://jso.dev/ADNLPModels.jl) which provides automatic differentiation (AD)-based model implementations that conform to the [NLPModels.jl](https://github.com/JuliaSmoothOptimizers/ADNLPModels.jl) API.
 
 The last symbol is what distinguish the two available methods. It corresponds to the NLP solver. By default, we