[105] | 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 <stat/libFN.h> |
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| 15 | #include <stat/emix.h> |
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[117] | 16 | #include <estim/ekf_templ.h> |
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[105] | 17 | #include <estim/libPF.h> |
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| 18 | |
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| 19 | #include "pmsm.h" |
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| 20 | #include "simulator.h" |
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[117] | 21 | #include "sim_profiles.h" |
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[105] | 22 | |
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| 23 | #include <stat/loggers.h> |
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| 24 | |
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| 25 | using namespace itpp; |
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| 26 | |
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| 27 | int main() { |
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| 28 | // Kalman filter |
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| 29 | int Ndat = 90000; |
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| 30 | double h = 1e-6; |
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| 31 | int Nsimstep = 125; |
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[131] | 32 | int Npar = 10; |
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[105] | 33 | |
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| 34 | dirfilelog L("exp/pmsm_mix",1000); |
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| 35 | //memlog L(Ndat); |
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| 36 | |
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| 37 | // SET SIMULATOR |
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| 38 | pmsmsim_set_parameters ( 0.28,0.003465,0.1989,0.0,4,1.5,0.04, 200., 3e-6, h ); |
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| 39 | double Ww = 0.0; |
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| 40 | vec dt ( 2 ); |
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| 41 | vec ut ( 2 ); |
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| 42 | vec xtm=zeros ( 4 ); |
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| 43 | vec xdif=zeros ( 4 ); |
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| 44 | vec xt ( 4 ); |
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| 45 | vec ddif=zeros(2); |
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| 46 | IMpmsm fxu; |
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| 47 | // Rs Ls dt Fmag(Ypm) kp p J Bf(Mz) |
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| 48 | fxu.set_parameters ( 0.28, 0.003465, Nsimstep*h, 0.1989, 1.5 ,4.0, 0.04, 0.0 ); |
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| 49 | OMpmsm hxu; |
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| 50 | mat Qt=zeros ( 4,4 ); |
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| 51 | mat Rt=zeros ( 2,2 ); |
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| 52 | |
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| 53 | // ESTIMATORS |
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| 54 | vec mu0= "0.0 0.0 0.0 0.0"; |
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| 55 | vec Qdiag ( "62 66 454 0.03" ); //zdenek: 0.01 0.01 0.0001 0.0001 |
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| 56 | vec Rdiag ( "100 100" ); //var(diff(xth)) = "0.034 0.034" |
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| 57 | mat Q =diag( Qdiag ); |
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| 58 | mat R =diag ( Rdiag ); |
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| 59 | EKFfull Efix ( rx,ry,ru ); |
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| 60 | Efix.set_est ( mu0, 1*eye ( 4 ) ); |
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| 61 | Efix.set_parameters ( &fxu,&hxu,Q,R); |
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| 62 | |
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| 63 | RV rQR("10 11", "{Q R }", "4 2 ","0 0"); |
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| 64 | EKFful_unQR EKU (rx,ry,ru,rQR); |
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| 65 | EKU.set_est ( mu0, 1*ones ( 4 ) ); |
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| 66 | EKU.set_parameters ( &fxu,&hxu,diag(Qdiag),diag(Rdiag) ); |
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| 67 | |
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| 68 | //QU model |
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| 69 | egamma Gcom(rQR);Gcom.set_parameters(ones(6),vec("1 1 1e4 1e10 1 1")); |
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[117] | 70 | /* cout << Gcom.mean() <<endl; |
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| 71 | cout << Gcom.sample() <<endl;*/ |
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| 72 | euni Ucom(rQR); Ucom.set_parameters(zeros(6),vec("60 60 453 0.03 100 100")); |
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| 73 | /* cout << Ucom.mean() <<endl; |
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| 74 | cout << Ucom.sample() <<endl;*/ |
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[105] | 75 | Array<epdf*> Coms(2); |
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| 76 | Coms(0) = &Gcom; |
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| 77 | Coms(1) = &Ucom; |
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[117] | 78 | emix Eevol(rQR); Eevol.set_parameters("0.1 0.9", Coms); |
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| 79 | // cout << Eevol.sample() <<endl; |
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[105] | 80 | |
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[145] | 81 | mepdf evolQR(Eevol); |
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[105] | 82 | MPF<EKFful_unQR> M ( rx,rQR, evolQR, evolQR, Npar, EKU ); |
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[125] | 83 | M.set_est ( evolQR._epdf() ); |
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[105] | 84 | |
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| 85 | epdf& Efix_ep = Efix._epdf(); |
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| 86 | epdf& M_ep = M._epdf(); |
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| 87 | |
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| 88 | //LOG |
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| 89 | RV rUD("12 13 14 15", "{u_isa u_isb i_isa i_isb }", ones_i(4),zeros_i(4)); |
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| 90 | int X_log = L.add(rx,"X"); |
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| 91 | int Efix_log = L.add(rx,"XF"); |
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[117] | 92 | int M_log = L.add(concat(rQR,rx),"M"); |
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[105] | 93 | L.init(); |
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| 94 | |
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[117] | 95 | double dum=0.0; |
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| 96 | vec dumvec = vec_1(1.0); |
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| 97 | vec z= evolQR.samplecond(dumvec,dum) ; |
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| 98 | cout << z << endl; |
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| 99 | |
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[105] | 100 | for ( int tK=1;tK<Ndat;tK++ ) { |
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| 101 | //Number of steps of a simulator for one step of Kalman |
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| 102 | for ( int ii=0; ii<Nsimstep;ii++ ) { |
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[117] | 103 | sim_profile_steps1 ( Ww, true ); |
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[105] | 104 | pmsmsim_step ( Ww ); |
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| 105 | }; |
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| 106 | // simulation via deterministic model |
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| 107 | ut ( 0 ) = KalmanObs[0]; |
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| 108 | ut ( 1 ) = KalmanObs[1]; |
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| 109 | dt ( 0 ) = KalmanObs[2]; |
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| 110 | dt ( 1 ) = KalmanObs[3]; |
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| 111 | |
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| 112 | //ESTIMATE |
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| 113 | Efix.bayes(concat(dt,ut)); |
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| 114 | // |
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| 115 | M.bayes(concat(dt,ut)); |
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| 116 | |
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| 117 | //LOG |
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| 118 | L.logit(X_log, vec(x,4)); //vec from C-array |
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| 119 | L.logit(Efix_log, Efix_ep.mean() ); |
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| 120 | L.logit(M_log, M_ep.mean() ); |
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| 121 | |
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| 122 | L.step(false); |
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| 123 | } |
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| 124 | |
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| 125 | L.step(true); |
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| 126 | //L.itsave("sim_var.it"); |
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| 127 | |
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| 128 | |
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| 129 | return 0; |
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| 130 | } |
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