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

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

using namespace itpp;
/*
// 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 = 10000;
	double h = 1e-6;
	int Nsimstep = 20;
	
	const int Nll = 10;

//	cout << KF;
	// internal model
	IMpmsm 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 ( "0.03 0.03 0.001 0.00001" );
	vec Rdiag ( "0.000001 0.000001" ); //var(diff(xth)) = "0.034 0.034"
	vec vQ = "0.01:0.01:0.1";
	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 ( Nll,Ndat );

	EKFCh* kfArray[Nll];


	for ( int i=0;i<Nll;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 ( 100*ones ( 4 ) ) );
		kfArray[i]->set_parameters ( &fxu,&hxu,chmat ( Qid ),R );
	}

	epdf& KFEep = KFE._epdf();

	mat Xt=zeros ( 9,Ndat ); //true state from simulator
	mat Dt=zeros ( 4,Ndat ); //observation
	mat XtE=zeros ( 4,Ndat );

	// SET SIMULATOR
	pmsmsim_set_parameters ( 0.28,0.003465,0.1989,0.0,4,1.5,0.04, 200., 3e-6, h );
	double Ww=0.0;
	static int k_rampa=1;
	static long k_rampa_tmp=0;
	vec dt ( 2 );
	vec ut ( 2 );

	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
			Ww+=k_rampa*2.*M_PI*2e-4;    //1000Hz/s
			if ( Ww>2.*M_PI*150. ) {
				Ww=2.*M_PI*150.;
				if ( k_rampa_tmp<500000 ) k_rampa_tmp++;
				else {k_rampa=-1;k_rampa_tmp=0;}
			};
			if ( Ww<-2.*M_PI*150. ) Ww=-2.*M_PI*150.; /* */

			pmsmsim_step ( Ww );
		};
		// collect data
		ut ( 0 ) = KalmanObs[0];
		ut ( 1 ) = KalmanObs[1];
		dt ( 0 ) = KalmanObs[2];
		dt ( 1 ) = KalmanObs[3];
		//estimator
		KFE.bayes ( concat ( dt,ut ) );
		for ( int i=0;i<Nll;i++ ) {
			kfArray[i]->bayes ( concat ( dt,ut ) );
			ll ( i,tK ) =ll ( i,tK-1 ) + kfArray[i]->_ll();
		}

		Xt.set_col ( tK,vec ( x,9 ) );
		Dt.set_col ( tK, concat ( dt,ut ) );
		XtE.set_col ( tK,KFEep.mean() );
	}

	it_file fou ( "pmsm_sim.it" );

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

}