[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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[218] | 20 | #include "sim_profiles.h" |
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[42] | 21 | |
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| 22 | using namespace itpp; |
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[48] | 23 | //!Extended Kalman filter with unknown \c Q |
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| 24 | class EKF_unQ : public EKFCh , public BMcond { |
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[42] | 25 | public: |
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[48] | 26 | //! Default constructor |
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| 27 | EKF_unQ ( RV rx, RV ry,RV ru,RV rQ ) :EKFCh ( rx,ry,ru ),BMcond ( rQ ) {}; |
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| 28 | void condition ( const vec &Q0 ) { |
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| 29 | Q.setD ( Q0,0 ); |
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| 30 | //from EKF |
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| 31 | preA.set_submatrix ( dimy+dimx,dimy,Q._Ch() ); |
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[54] | 32 | }; |
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[48] | 33 | }; |
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[42] | 34 | |
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| 35 | |
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| 36 | int main() { |
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| 37 | // Kalman filter |
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[218] | 38 | int Ndat = 9000; |
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[42] | 39 | double h = 1e-6; |
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[48] | 40 | int Nsimstep = 125; |
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[218] | 41 | int Npart = 200; |
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[54] | 42 | |
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[42] | 43 | // internal model |
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| 44 | IMpmsm fxu; |
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[218] | 45 | // Rs Ls dt Fmag(Ypm) kp p J Bf(Mz) |
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| 46 | fxu.set_parameters ( 0.28, 0.003465, Nsimstep*h, 0.1989, 1.5 ,4.0, 0.04, 0.0 ); |
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[42] | 47 | // observation model |
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| 48 | OMpmsm hxu; |
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| 49 | |
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| 50 | vec mu0= "0.0 0.0 0.0 0.0"; |
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[218] | 51 | vec Qdiag ( "0.001 0.001 1e-6 1e-10" ); //zdenek: 0.01 0.01 0.0001 0.0001 |
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| 52 | vec Rdiag ( "1e-10 1e-10" ); //var(diff(xth)) = "0.034 0.034" |
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[42] | 53 | chmat Q ( Qdiag ); |
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| 54 | chmat R ( Rdiag ); |
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| 55 | EKFCh KFE ( rx,ry,ru ); |
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[48] | 56 | KFE.set_parameters ( &fxu,&hxu,Q,R ); |
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[54] | 57 | KFE.set_est ( mu0, chmat ( 1*ones ( 4 ) ) ); |
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[42] | 58 | |
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[162] | 59 | RV rQ ( "{Q }","2" ); |
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[48] | 60 | EKF_unQ KFEp ( rx,ry,ru,rQ ); |
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| 61 | KFEp.set_parameters ( &fxu,&hxu,Q,R ); |
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[218] | 62 | KFEp.set_est ( mu0, chmat ( zeros ( 4 ) ) ); |
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[42] | 63 | |
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[48] | 64 | mgamma evolQ ( rQ,rQ ); |
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| 65 | MPF<EKF_unQ> M ( rx,rQ,evolQ,evolQ,Npart,KFEp ); |
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| 66 | // initialize |
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[218] | 67 | evolQ.set_parameters ( 10.0 ); //sigma = 1/10 mu |
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[54] | 68 | evolQ.condition ( "0.01 0.01" ); //Zdenek default |
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[48] | 69 | epdf& pfinit=evolQ._epdf(); |
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| 70 | M.set_est ( pfinit ); |
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[218] | 71 | evolQ.set_parameters ( 10.0 ); |
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[42] | 72 | |
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[48] | 73 | // |
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[42] | 74 | |
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[170] | 75 | const epdf& KFEep = KFE._epdf(); |
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| 76 | const epdf& Mep = M._epdf(); |
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[42] | 77 | |
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[54] | 78 | mat Xt=zeros ( Ndat ,9 ); //true state from simulator |
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| 79 | mat Dt=zeros ( Ndat,4+2 ); //observation |
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| 80 | mat XtE=zeros ( Ndat, 4 ); |
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[218] | 81 | mat XtM=zeros ( Ndat,2+4 ); //Q + x |
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[42] | 82 | |
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| 83 | // SET SIMULATOR |
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| 84 | pmsmsim_set_parameters ( 0.28,0.003465,0.1989,0.0,4,1.5,0.04, 200., 3e-6, h ); |
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| 85 | double Ww=0.0; |
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| 86 | static int k_rampa=1; |
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| 87 | static long k_rampa_tmp=0; |
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| 88 | vec dt ( 2 ); |
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| 89 | vec ut ( 2 ); |
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[218] | 90 | vec vecW="0.1 0.2 0.4 0.9 0.4 0.2 0.0 -0.4 -0.9 -1.6 -0.4 0.0 0.0"; |
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[42] | 91 | |
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| 92 | for ( int tK=1;tK<Ndat;tK++ ) { |
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| 93 | //Number of steps of a simulator for one step of Kalman |
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| 94 | for ( int ii=0; ii<Nsimstep;ii++ ) { |
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| 95 | //simulator |
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[218] | 96 | sim_profile_vec01t(Ww,vecW); |
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[42] | 97 | pmsmsim_step ( Ww ); |
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| 98 | }; |
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| 99 | // collect data |
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[218] | 100 | ut ( 0 ) = 0.0;//KalmanObs[0]; |
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| 101 | ut ( 1 ) = 0.0;//KalmanObs[1]; |
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[42] | 102 | dt ( 0 ) = KalmanObs[2]; |
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| 103 | dt ( 1 ) = KalmanObs[3]; |
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[48] | 104 | |
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[42] | 105 | //estimator |
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| 106 | KFE.bayes ( concat ( dt,ut ) ); |
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[48] | 107 | M.bayes ( concat ( dt,ut ) ); |
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[42] | 108 | |
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[54] | 109 | Xt.set_row ( tK,vec ( x,9 ) ); //vec from C-array |
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| 110 | Dt.set_row ( tK, concat ( dt,ut,vec_1(sqrt(pow(ut(0),2)+pow(ut(1),2))), vec_1(sqrt(pow(dt(0),2)+pow(dt(1),2))) ) ); |
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| 111 | XtE.set_row ( tK,KFEep.mean() ); |
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| 112 | XtM.set_row ( tK,Mep.mean() ); |
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[42] | 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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[48] | 120 | fou << Name ( "xthM" ) << XtM; |
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[42] | 121 | //Exit program: |
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[54] | 122 | |
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[42] | 123 | return 0; |
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[54] | 124 | } |
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