root/pmsm/mpf_u_delta.cpp @ 225

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

  \ingroup PMSM

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

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


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

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

using namespace itpp;

//!Extended Kalman filter with unknown \c Q
class EKFCh_cond : public EKFCh , public BMcond {
public:
        //! Default constructor
        EKFCh_cond ( RV rx, RV ry,RV ru,RV rC ) :EKFCh ( rx,ry,ru ),BMcond ( rC ) {};
        void condition ( const vec &val ) {
                pfxu->condition( val );
        };
};

class IMpmsm_delta :  public IMpmsm {
        protected:
                vec ud;
        public:
                IMpmsm_delta() :IMpmsm(),ud(2) {};
        //! Set mechanical and electrical variables

                void condition(const vec &val){ud = val;}
                vec eval ( const vec &x0, const vec &u0 ) {
                // last state
                        double iam = x0 ( 0 );
                        double ibm = x0 ( 1 );
                        double omm = x0 ( 2 );
                        double thm = x0 ( 3 );
                        double uam = u0 ( 0 );
                        double ubm = u0 ( 1 );

                        vec xk=zeros ( 4 );
                //ia
                        xk ( 0 ) = ( 1.0- Rs/Ls*dt ) * iam + Ypm/Ls*dt*omm * sin ( thm ) + (uam+ud(0))*dt/Ls;
                //ib
                        xk ( 1 ) = ( 1.0- Rs/Ls*dt ) * ibm - Ypm/Ls*dt*omm * cos ( thm ) + (ubm+ud(1))*dt/Ls;
                //om
                        xk ( 2 ) = omm + kp*p*p * Ypm/J*dt* ( ibm * cos ( thm )-iam * sin ( thm ) ) - p/J*dt*Mz;
                //th
                        xk ( 3 ) = thm + omm*dt; // <0..2pi>
                        if ( xk ( 3 ) >pi ) xk ( 3 )-=2*pi;
                        if ( xk ( 3 ) <-pi ) xk ( 3 ) +=2*pi;
                        return xk;
                }

};


int main() {
        // Kalman filter
        int Ndat = 9000;
        double h = 1e-6;
        int Nsimstep = 125;
        int Npart = 200;

        // internal model
        IMpmsm_delta fxu;
        //                  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
        OMpmsm hxu;

        vec mu0= "0.0 0.0 0.0 0.0";
        vec Qdiag ( "0.6 0.6 0.001 0.000001" ); //zdenek: 0.01 0.01 0.0001 0.0001
        vec Rdiag ( "1e-8 1e-8" ); //var(diff(xth)) = "0.034 0.034"
        chmat Q ( Qdiag );
        chmat R ( Rdiag );
        EKFCh KFE ( rx,ry,ru );
        KFE.set_parameters ( &fxu,&hxu,Q,R );
        KFE.set_est ( mu0, chmat ( zeros ( 4 ) ) );

        RV rUd ( "{ud }", "2" );
        EKFCh_cond KFEp ( rx,ry,ru,rUd );
        KFEp.set_parameters ( &fxu,&hxu,Q,R );
        KFEp.set_est ( mu0, chmat ( zeros ( 4 ) ) );

        mlnorm<ldmat> evolUd ( rUd,rUd );
        MPF<EKFCh_cond> M ( rx,rUd,evolUd,evolUd,Npart,KFEp );
        // initialize
        vec Ud0="0 0";
        evolUd.set_parameters ( eye(2), vec_2(0.0,0.0), ldmat(eye(2)) ); 
        evolUd.condition (Ud0 );
        epdf& pfinit=evolUd._epdf();
        M.set_est ( pfinit );
        evolUd.set_parameters ( eye(2), vec_2(0.0,0.0), ldmat(0.1*eye(2)) ); 
        
        mat Xt=zeros ( Ndat ,4 ); //true state from simulator
        mat Dt=zeros ( Ndat,2+2 ); //observation
        mat XtE=zeros ( Ndat, 4 );
        mat Qtr=zeros ( Ndat, 4 );
        mat XtM=zeros ( Ndat,2+4 ); //W + x

        // 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="1 2 4 9 4 2 0 -4 -9 -16 -4 0 0 0";
        vecW*=10.0;

        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[0];
                ut(1) = KalmanObs[1];
                dt(0) = KalmanObs[2];
                dt(1) = KalmanObs[3];
                xt = vec(x,4);
                
                //estimator
                KFE.bayes ( concat ( dt,ut ) );
                M.bayes ( concat ( dt,ut ) );

                Xt.set_row ( tK, xt); //vec from C-array
                Dt.set_row ( tK, concat ( dt,ut));
                Qtr.set_row ( tK, Qdiag);
                XtE.set_row ( tK,KFE._e()->mean() );
                XtM.set_row ( tK,M._e()->mean() );
        }

        it_file fou ( "mpf_u_delta.it" );

        fou << Name ( "xth" ) << Xt;
        fou << Name ( "Dt" ) << Dt;
        fou << Name ( "Qtr" ) << Qtr;
        fou << Name ( "xthE" ) << XtE;
        fou << Name ( "xthM" ) << XtM;
        //Exit program:

        return 0;
}