root/pmsm/pmsm_unkQ.cpp @ 264

/*
  \file
  \brief Models for synchronous electric drive using IT++ and BDM
  \author Vaclav Smidl.

  -----------------------------------
  BDM++ - C++ library for Bayesian Decision Making under Uncertainty

  Using IT++ for numerical operations
  -----------------------------------
*/


#include <estim/libKF.h>
#include <estim/libPF.h>
#include <stat/libFN.h>

#include "pmsm.h"

using namespace bdm;
/*
// PMSM with Q on Ia and Ib given externally
class EKF_unQ : public EKF<chmat> , public BMcond {
public:
        EKF_unQ( rx,ry,ru,rQ):EKF<chmat>(rx,ry,ru),BMcond(rQ){};
        void condition(const vec &Q0){};
};*/


int main() {
        // Kalman filter
        int Ndat = 1000;

//      cout << KF;
        // internal model
        IMpmsm fxu;
        //                  Rs    Ls        dt       Fmag(Ypm) kp   p    J     Bf(Mz)
        fxu.set_parameters ( 0.28, 0.003465, 20*1e-6, 0.1989,   1.5 ,4.0, 0.04, 0.0 );
        // observation model
        OMpmsm hxu;

        vec mu0= "100 100 100 1";
        vec Qdiag ( "0.1 0.1 0.01 0.01" );
        vec Rdiag ( "0.02 0.02" );
        vec vQ = "0.01:0.01:100";
        chmat Q ( Qdiag );
        chmat R ( Rdiag );
        EKFCh KFE ( rx,ry,ru );
        KFE.set_est ( mu0, chmat ( 1000*ones ( 4 ) ) );
        KFE.set_parameters ( &fxu,&hxu,Q,R );

        mat ll(100,Ndat);

        EKFCh* kfArray[100];

        for ( int i=0;i<100;i++ ) {
                vec Qid ( Qdiag );
                Qid ( 0 ) = vQ ( i ); Qid ( 1 ) = vQ ( i );
                kfArray[i]= new EKFCh ( rx,ry,ru );
                kfArray[i]->set_est ( mu0, chmat ( 1000*ones ( 4 ) ) );
                kfArray[i]->set_parameters ( &fxu,&hxu,chmat ( Qid ),R );
        }

        const epdf& KFEep = KFE._epdf();

        //simulator values
        vec dt ( 2 );
        vec wt ( 2 );
        vec ut ( 2 );
        vec xt=mu0;
        vec et ( 4 );

        mat Xt=zeros ( 4,Ndat );
        mat XtE=zeros ( 4,Ndat );
        Xt.set_col ( 0,KFEep.mean() );

        for ( int t=1;t<Ndat;t++ ) {
                //simulator
                UniRNG.sample_vector ( 2,wt );

                if ( rem ( t,500 ) <200 ) ut = rem ( t,500 ) *ones ( 2 );
                else
                        ut=zeros ( 2 );

                NorRNG.sample_vector ( 4,et );
                NorRNG.sample_vector ( 2,wt );
                xt = fxu.eval ( xt,ut ) + Q.sqrt_mult ( et );
                dt = hxu.eval ( xt,ut ) + R.sqrt_mult ( wt );

                //estimator
                KFE.bayes ( concat ( dt,ut ) );
                for ( int i=0;i<100;i++ ) {kfArray[i]->bayes( concat ( dt,ut ) );ll(i,t)=ll(i,t-1) + kfArray[i]->_ll();
                }
                
                Xt.set_col ( t,xt );
                XtE.set_col ( t,KFEep.mean() );
        }

        it_file fou ( "pmsm.it" );

        fou << Name ( "xth" ) << Xt;
        fou << Name ( "xthE" ) << XtE;
        fou << Name ( "ll" ) << ll;
        //Exit program:
        return 0;

}