@jbcaillau To get the OptimalControl model for the beam problem, we have to make:

docp = beam(OptimalControlBackend())

Since, the models for exa are also with OptimalControl, we can have:

docp = beam(OptimalControlBackend(:adnlp))
docp = beam(OptimalControlBackend(:exa))

But now, I have added arguments and keyword arguments to the signature of the problems that are passed to the function CTDirect.direct_transcription, and this function as the following signature:

function direct_transcription(
    ocp::CTModels.Model,
    description...;
    grid_size=__grid_size(),
    disc_method=__disc_method(),
    time_grid=__time_grid(),
    init=__ocp_init(),
    lagrange_to_mayer=true,
    kwargs...,
)

where

[description]: set the NLP model ([`:adnlp`] or `:exa`) and / or solver ([`:ipopt`], :madnlp or :knitro)

so technically, it would be possible to write something like:

docp = beam(OptimalControlBackend(:exa), :adnlp, :madnlp; grid_size=100, disc_method=:euler)

So another possibility is to indicate that the problem is scalarised:

docp = beam(OptimalControlBackend(:scalarised), :adnlp)
docp = beam(OptimalControlBackend(:scalarised), :exa)

and this works too

docp = beam(OptimalControlBackend(), :adnlp) # this is the default

but this does not work:

docp = beam(OptimalControlBackend(), :exa) # the dynamics and constraints must be scalarised.

[Dev] Adding models for exa #88

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