Using the option Method -> "NumericDerivative" in SystemModelLinearize may work better for this model when selecting "InitialValues" as operating point.

Dear Sergio,

Thank you very much for your reply again.

I tried making the example even simpler. Instead of using a revolute joint driven by an rotational spring, I adapted the revolute constraint to accept a torque. The modification worked using the numerical linearization method, but the symbolic one still fails.

I have the impression it's not possible to make the example simpler. It's just a single angular spring acting on a single body with rotational inertia. It's the angular equivalent of the classical spring with a mass attached. I'm not an expert, but I guess the difficulty lies in dealing with the three-dimensional space rotations matrices of the MultiBody library.

Sadly, for the project I have in mind, this limitation rules out the use of System Modeler (and maybe Modelica) with Mathematica. Is there a way to know if future development of SystemModelLinearize would include dealing with this kind of problem? This information would be useful for me to decide a way forward.

Thank you very much.

Regards,

Fabián

Dear Sergio,

Thank you very much for your reply.

Using "NumericDerivative" as an option does indeed produce the correct result.

I wonder if there is a workaround that would allow me to use "SymbolicDerivative" instead. In my model, I require to keep all the spring constants symbolic during linearization, in order to later replace them by complex values and analyze the transfer functions in frequency space.

I tried using "SymbolicDerivative" with "EquilibriumValues" instead of "InitialValues". However, after a long time, it failed with a message saying that I should use "NumericDerivative". In my model, the initial values equal the equilibrium values.

By any chance, is there a way to identify the feature in my model that produces this condition? In such a case, perhaps I would be able to modify my model accordingly.

Any suggestion would be highly appreciated.

Cheers,

Fabián