[42] | 1 | /* |
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| 2 | \file |
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| 3 | \brief Models for synchronous electric drive using IT++ and BDM |
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| 4 | \author Vaclav Smidl. |
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| 5 | |
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| 6 | ----------------------------------- |
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| 7 | BDM++ - C++ library for Bayesian Decision Making under Uncertainty |
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| 8 | |
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| 9 | Using IT++ for numerical operations |
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| 10 | ----------------------------------- |
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| 11 | */ |
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| 12 | |
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| 13 | #include <itpp/itbase.h> |
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| 14 | #include <estim/libKF.h> |
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| 15 | #include <estim/libPF.h> |
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| 16 | #include <stat/libFN.h> |
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| 17 | |
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| 18 | #include "pmsm.h" |
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| 19 | #include "simulator.h" |
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| 20 | |
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| 21 | using namespace itpp; |
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| 22 | /* |
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| 23 | // PMSM with Q on Ia and Ib given externally |
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| 24 | class EKF_unQ : public EKF<chmat> , public BMcond { |
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| 25 | public: |
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| 26 | EKF_unQ( rx,ry,ru,rQ):EKF<chmat>(rx,ry,ru),BMcond(rQ){}; |
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| 27 | void condition(const vec &Q0){}; |
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| 28 | };*/ |
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| 29 | |
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| 30 | |
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| 31 | int main() { |
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| 32 | // Kalman filter |
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| 33 | int Ndat = 10000; |
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| 34 | double h = 1e-6; |
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| 35 | int Nsimstep = 20; |
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| 36 | |
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[44] | 37 | const it Nll = 10; |
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[42] | 38 | |
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| 39 | // cout << KF; |
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| 40 | // internal model |
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| 41 | IMpmsm fxu; |
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| 42 | // Rs Ls dt Fmag(Ypm) kp p J Bf(Mz) |
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| 43 | fxu.set_parameters ( 0.28, 0.003465, Nsimstep*h, 0.1989, 1.5 ,4.0, 0.04, 0.0 ); |
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| 44 | // observation model |
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| 45 | OMpmsm hxu; |
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| 46 | |
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| 47 | vec mu0= "0.0 0.0 0.0 0.0"; |
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| 48 | vec Qdiag ( "0.03 0.03 0.001 0.00001" ); |
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| 49 | vec Rdiag ( "0.000001 0.000001" ); //var(diff(xth)) = "0.034 0.034" |
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| 50 | vec vQ = "0.01:0.01:0.1"; |
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| 51 | chmat Q ( Qdiag ); |
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| 52 | chmat R ( Rdiag ); |
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| 53 | EKFCh KFE ( rx,ry,ru ); |
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| 54 | KFE.set_est ( mu0, chmat ( 1000*ones ( 4 ) ) ); |
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| 55 | KFE.set_parameters ( &fxu,&hxu,Q,R ); |
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| 56 | |
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| 57 | mat ll ( Nll,Ndat ); |
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| 58 | |
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| 59 | EKFCh* kfArray[Nll]; |
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| 60 | |
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| 61 | |
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| 62 | for ( int i=0;i<Nll;i++ ) { |
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| 63 | vec Qid ( Qdiag ); |
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| 64 | Qid ( 0 ) = vQ ( i ); Qid ( 1 ) = vQ ( i ); |
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| 65 | kfArray[i]= new EKFCh ( rx,ry,ru ); |
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| 66 | kfArray[i]->set_est ( mu0, chmat ( 100*ones ( 4 ) ) ); |
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| 67 | kfArray[i]->set_parameters ( &fxu,&hxu,chmat ( Qid ),R ); |
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| 68 | } |
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| 69 | |
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| 70 | epdf& KFEep = KFE._epdf(); |
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| 71 | |
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| 72 | mat Xt=zeros ( 9,Ndat ); //true state from simulator |
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| 73 | mat Dt=zeros ( 4,Ndat ); //observation |
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| 74 | mat XtE=zeros ( 4,Ndat ); |
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| 75 | |
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| 76 | // SET SIMULATOR |
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| 77 | pmsmsim_set_parameters ( 0.28,0.003465,0.1989,0.0,4,1.5,0.04, 200., 3e-6, h ); |
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| 78 | double Ww=0.0; |
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| 79 | static int k_rampa=1; |
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| 80 | static long k_rampa_tmp=0; |
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| 81 | vec dt ( 2 ); |
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| 82 | vec ut ( 2 ); |
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| 83 | |
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| 84 | for ( int tK=1;tK<Ndat;tK++ ) { |
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| 85 | //Number of steps of a simulator for one step of Kalman |
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| 86 | for ( int ii=0; ii<Nsimstep;ii++ ) { |
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| 87 | //simulator |
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| 88 | Ww+=k_rampa*2.*M_PI*2e-4; //1000Hz/s |
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| 89 | if ( Ww>2.*M_PI*150. ) { |
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| 90 | Ww=2.*M_PI*150.; |
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| 91 | if ( k_rampa_tmp<500000 ) k_rampa_tmp++; |
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| 92 | else {k_rampa=-1;k_rampa_tmp=0;} |
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| 93 | }; |
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| 94 | if ( Ww<-2.*M_PI*150. ) Ww=-2.*M_PI*150.; /* */ |
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| 95 | |
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| 96 | pmsmsim_step ( Ww ); |
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| 97 | }; |
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| 98 | // collect data |
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| 99 | ut ( 0 ) = KalmanObs[0]; |
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| 100 | ut ( 1 ) = KalmanObs[1]; |
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| 101 | dt ( 0 ) = KalmanObs[2]; |
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| 102 | dt ( 1 ) = KalmanObs[3]; |
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| 103 | //estimator |
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| 104 | KFE.bayes ( concat ( dt,ut ) ); |
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| 105 | for ( int i=0;i<Nll;i++ ) { |
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| 106 | kfArray[i]->bayes ( concat ( dt,ut ) ); |
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| 107 | ll ( i,tK ) =ll ( i,tK-1 ) + kfArray[i]->_ll(); |
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| 108 | } |
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| 109 | |
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| 110 | Xt.set_col ( tK,vec ( x,9 ) ); |
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| 111 | Dt.set_col ( tK, concat ( dt,ut ) ); |
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| 112 | XtE.set_col ( tK,KFEep.mean() ); |
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| 113 | } |
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| 114 | |
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| 115 | it_file fou ( "pmsm_sim.it" ); |
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| 116 | |
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| 117 | fou << Name ( "xth" ) << Xt; |
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| 118 | fou << Name ( "Dt" ) << Dt; |
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| 119 | fou << Name ( "xthE" ) << XtE; |
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| 120 | fou << Name ( "ll" ) << ll; |
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| 121 | fou << Name ( "llgrid" ) << vQ; |
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| 122 | //Exit program: |
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| 123 | return 0; |
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| 124 | |
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| 125 | } |
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