I am trying to fit data to a "First Order Plus Dead Time" model. I can optimize the fit by using the solution to the FOPDT equation, but, simply as an intellectual exercise, I would like to also be able to do this using the FOPDT differential equation. I have Python code that can do this (it takes an incredibly long time to run; I would guess that Mathcad would take much more time).
The nub problem of the problem as I see it, is that I think I need an optimization solve block to do the least squares fit which, in turn, then needs to call an odesolve solve block with parameters.
I quickly realized that I don't have a clue as to how to do this, and I haven't found any "parameterized solve block" examples that return odesolve solutions.
Attached are my Prime 6 worksheet, text file of data, and PDF of worksheet.
Solved! Go to Solution.
Unfortunately there is no way to convert a Prime file to real Mathcad format.
Attached is a pdf print of the file. I only made the few changes to the original file that are highlighted in yellow just to show how the solve block could be parametrized.
FWIW, here is my final P6 worksheet which utilizes Werner's suggestion plus a modified optimization to be able to repetitively call the odesolve block.
Shame on me!
In my sheet y.ode never changes and uses just the guess values!
I forgot to make y.ode dependent on the current values of K, theta and tau 😞