> On Sat, Oct 20, 2018 at 1:01 AM Nicklas Karlsson < > nicklas.karlsso...@gmail.com> wrote: > > > (1.) > > Then using state space model and integrator I want to integrate error > > between where signal is expected to be and there measurement suggest it is. > > As is now I integrate error between reference signal measured output and > > then gain is reduced to get a more stable system the integrator will add an > > overshoot. Basically my idea is to integrate only model error. > > > > (2.) > > For a linear state space model there must be a "standardized" method to > > calculate feed forward. This is of particular interest then gain is reduced > > to get a more stable system. > > > > (1)Integrators almost always introduce an overshoot. I am pretty sure you > always want to integrate the difference between where you are and where you > want to be (reference). Adding an integrator on the difference between > model error and measurement sounds a lot like "residual whitening." A > Kalman filter can be set up to estimate unknown model parameters. The > unknown parameters are set up as states of the system model. > (2)I think most people set FF gains from system knowledge and previous > experience. There are optimal control techniques that will calculate them > for you. H infinity and related control schemes. > > The Julia language has toolboxes for Kalman filters (and variations) and > control theory. My guess is that you can find a system model that matches > your machine if you look. Might require a friend at a university to d/l > some papers for you. > Eric Keller > Boalsburg, Pennsylvania
No not interested in any of the above more than possible what you write about integration. _______________________________________________ Emc-users mailing list Emc-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/emc-users
