root/applications/pmsm/TR2245/unitsteps.cpp @ 1410

/*!
  \file
  \brief TR 2525 file for testing Toy Problem of mpf for Covariance Estimation
  \author Vaclav Smidl.

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

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



#include <estim/particles.h>
#include <estim/ekf_template.h>
#include <base/loggers.h>


#include "../pmsm.h"
#include "simulator.h"
#include "../sim_profiles.h"

using namespace bdm;

int main ( int argc, char* argv[] ) {
        const char *fname;
        if ( argc>1 ) {fname = argv[1]; }
        else { fname = "unitsteps.cfg"; }
        UIFile F ( fname );

        double h = 1e-6;
        int Nsimstep = 125;


        // Kalman filter
        int Ndat;
        int Npart;
        F.lookupValue ( "ndat", Ndat );
        F.lookupValue ( "Npart",Npart );
        shared_ptr<pdf> evolQ = UI::build<pdf>( F, "Qrw" );
        vec Qdiag;
        vec Rdiag;
        UI::get( Qdiag, F, "dQ" ); //( "1e-6 1e-6 0.001 0.0001" ); //zdenek: 0.01 0.01 0.0001 0.0001
        UI::get( Rdiag, F, "dR" );// ( "1e-8 1e-8" ); //var(diff(xth)) = "0.034 0.034"

        // internal model

        shared_ptr<IMpmsm> fxu= new IMpmsm;
//                  Rs    Ls        dt       Fmag(Ypm)    kp   p    J     Bf(Mz)
        fxu->set_parameters ( 0.28, 0.003465, Nsimstep*h, 0.1989, 1.5 ,4.0, 0.04, 0.0 );
        // observation model
        shared_ptr<OMpmsm> hxu=new OMpmsm;

        vec mu0= "0.0 0.0 0.0 0.0";
        chmat Q ( Qdiag );
        chmat R ( Rdiag );
        EKFCh KFE ;
        KFE.set_parameters ( fxu,hxu,Q,R );
        KFE.set_statistics ( mu0, chmat ( zeros ( 4 ) ) );
        KFE.set_rv ( rx );
        KFE.validate();

        RV rQ ( "{Q }","4" );
        RV rU ("{u }","2");
        RV rY ("{y }","2");
        EKFCh_dQ KFEp ;
        KFEp.set_parameters ( fxu,hxu,Q,R );
        KFEp.set_statistics ( mu0, chmat ( zeros ( 4 ) ) );
        KFEp.set_rv(rx);
        KFEp.set_yrv(rY); 
        KFEp.set_rvc(concat(rU, rQ)); 
        KFEp.validate();

        MPF M;
        evolQ->set_rv(rQ);
        M.set_pf ( evolQ,Npart );
        M._pf().set_statistics(ones(Npart), euni(zeros(4),2*Qdiag));
        M.set_BM(KFEp);
        M.set_yrv ( rY   );
        M.set_rvc ( rU   );
        M.validate();

        shared_ptr<dirfilelog> L = UI::build<dirfilelog>( F, "logger" );// ( "exp/mpf_test",100 );
        int l_X = L->add_vector ( rx, "xt" );
        int l_D = L->add_vector ( concat ( ry,ru ), "" );
        int l_Q= L->add_vector ( rQ, "" );

        KFE.log_level[ logbounds] = true;
        KFE.log_register ( *L,"KF" );
        M.log_level[logbounds] = true;
        M.log_register ( *L,"M" );
        L->init();

        // SET SIMULATOR
        pmsmsim_set_parameters ( 0.28,0.003465,0.1989,0.0,4,1.5,0.04, 200., 3e-6, h );
        vec dt ( 2 );
        vec ut ( 2 );
        vec xt ( 4 );
        vec xtm=zeros ( 4 );
        double Ww=0.0;
        vec vecW;
        UI::get( vecW, F, "profile" );

        for ( int tK=1;tK<Ndat;tK++ ) {
                //Number of steps of a simulator for one step of Kalman
                for ( int ii=0; ii<Nsimstep;ii++ ) {
                        //simulator
                        sim_profile_vec01t ( Ww,vecW );
                        pmsmsim_step ( Ww );
                };
                ut ( 0 ) = KalmanObs[4];
                ut ( 1 ) = KalmanObs[5];
                xt = fxu->eval ( xtm,ut ) + diag ( sqrt ( Qdiag ) ) *randn ( 4 );
                dt = hxu->eval ( xt,ut ) + diag(sqrt(Rdiag))*randn(2);
                xtm = xt;

                //Variances
                if ( tK==1000 )  Qdiag ( 0 ) *=10;
                if ( tK==2000 ) Qdiag ( 0 ) /=10;
                if ( tK==3000 )  Qdiag ( 1 ) *=10;
                if ( tK==4000 ) Qdiag ( 1 ) /=10;
                if ( tK==5000 )  Qdiag ( 2 ) *=10;
                if ( tK==6000 ) Qdiag ( 2 ) /=10;
                if ( tK==7000 )  Qdiag ( 3 ) *=10;
                if ( tK==8000 ) Qdiag ( 3 ) /=10;

                //estimator
                KFE.bayes (  dt,ut  );
                M.bayes ( dt,ut );

                L->log_vector ( l_X,xt );
                L->log_vector ( l_D,concat ( dt,ut ) );
                L->log_vector ( l_Q,Qdiag );

                KFE.log_write ( );
                M.log_write (  );
                L->step();
        }
        L->finalize();
        //Exit program:

        return 0;
}