[281] | 1 | /*! |
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| 2 | \file |
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| 3 | \brief TR 2525 file for testing Toy Problem of mpf for Covariance Estimation |
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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 | |
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| 14 | |
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[384] | 15 | #include <estim/particles.h> |
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| 16 | #include <estim/ekf_template.h> |
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[744] | 17 | #include <base/loggers.h> |
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[281] | 18 | |
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| 19 | |
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| 20 | #include "../pmsm.h" |
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| 21 | #include "simulator.h" |
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| 22 | #include "../sim_profiles.h" |
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| 23 | |
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| 24 | using namespace bdm; |
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| 25 | |
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| 26 | int main ( int argc, char* argv[] ) { |
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| 27 | const char *fname; |
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| 28 | if ( argc>1 ) {fname = argv[1]; } |
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| 29 | else { fname = "unitsteps.cfg"; } |
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[394] | 30 | UIFile F ( fname ); |
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[281] | 31 | |
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| 32 | double h = 1e-6; |
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| 33 | int Nsimstep = 125; |
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| 34 | |
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[357] | 35 | |
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| 36 | // Kalman filter |
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| 37 | int Ndat; |
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| 38 | int Npart; |
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| 39 | F.lookupValue ( "ndat", Ndat ); |
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| 40 | F.lookupValue ( "Npart",Npart ); |
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[744] | 41 | shared_ptr<pdf> evolQ = UI::build<pdf>( F, "Qrw" ); |
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[281] | 42 | vec Qdiag; |
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| 43 | vec Rdiag; |
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[357] | 44 | UI::get( Qdiag, F, "dQ" ); //( "1e-6 1e-6 0.001 0.0001" ); //zdenek: 0.01 0.01 0.0001 0.0001 |
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| 45 | UI::get( Rdiag, F, "dR" );// ( "1e-8 1e-8" ); //var(diff(xth)) = "0.034 0.034" |
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[285] | 46 | |
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[357] | 47 | // internal model |
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[281] | 48 | |
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[744] | 49 | shared_ptr<IMpmsm> fxu= new IMpmsm; |
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[285] | 50 | // Rs Ls dt Fmag(Ypm) kp p J Bf(Mz) |
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[744] | 51 | fxu->set_parameters ( 0.28, 0.003465, Nsimstep*h, 0.1989, 1.5 ,4.0, 0.04, 0.0 ); |
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[281] | 52 | // observation model |
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[744] | 53 | shared_ptr<OMpmsm> hxu=new OMpmsm; |
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[281] | 54 | |
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| 55 | vec mu0= "0.0 0.0 0.0 0.0"; |
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| 56 | chmat Q ( Qdiag ); |
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| 57 | chmat R ( Rdiag ); |
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| 58 | EKFCh KFE ; |
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[744] | 59 | KFE.set_parameters ( fxu,hxu,Q,R ); |
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| 60 | KFE.set_statistics ( mu0, chmat ( zeros ( 4 ) ) ); |
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[285] | 61 | KFE.set_rv ( rx ); |
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[744] | 62 | KFE.validate(); |
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[281] | 63 | |
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| 64 | RV rQ ( "{Q }","4" ); |
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[744] | 65 | RV rU ("{u }","2"); |
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| 66 | RV rY ("{y }","2"); |
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[294] | 67 | EKFCh_dQ KFEp ; |
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[744] | 68 | KFEp.set_parameters ( fxu,hxu,Q,R ); |
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| 69 | KFEp.set_statistics ( mu0, chmat ( zeros ( 4 ) ) ); |
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| 70 | KFEp.set_rv(rx); |
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| 71 | KFEp.set_yrv(rY); |
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| 72 | KFEp.set_rvc(concat(rU, rQ)); |
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| 73 | KFEp.validate(); |
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[285] | 74 | |
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[744] | 75 | MPF M; |
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| 76 | evolQ->set_rv(rQ); |
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| 77 | M.set_pf ( evolQ,Npart ); |
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| 78 | M._pf().set_statistics(ones(Npart), euni(zeros(4),2*Qdiag)); |
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| 79 | M.set_BM(KFEp); |
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| 80 | M.set_yrv ( rY ); |
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| 81 | M.set_rvc ( rU ); |
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| 82 | M.validate(); |
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[281] | 83 | |
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[744] | 84 | shared_ptr<dirfilelog> L = UI::build<dirfilelog>( F, "logger" );// ( "exp/mpf_test",100 ); |
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| 85 | int l_X = L->add_vector ( rx, "xt" ); |
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| 86 | int l_D = L->add_vector ( concat ( ry,ru ), "" ); |
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| 87 | int l_Q= L->add_vector ( rQ, "" ); |
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[285] | 88 | |
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[871] | 89 | KFE.log_level[ logbounds] = true; |
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[744] | 90 | KFE.log_register ( *L,"KF" ); |
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[871] | 91 | M.log_level[logbounds] = true; |
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[744] | 92 | M.log_register ( *L,"M" ); |
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[281] | 93 | L->init(); |
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| 94 | |
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| 95 | // SET SIMULATOR |
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| 96 | pmsmsim_set_parameters ( 0.28,0.003465,0.1989,0.0,4,1.5,0.04, 200., 3e-6, h ); |
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| 97 | vec dt ( 2 ); |
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| 98 | vec ut ( 2 ); |
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| 99 | vec xt ( 4 ); |
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| 100 | vec xtm=zeros ( 4 ); |
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| 101 | double Ww=0.0; |
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[357] | 102 | vec vecW; |
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| 103 | UI::get( vecW, F, "profile" ); |
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[281] | 104 | |
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| 105 | for ( int tK=1;tK<Ndat;tK++ ) { |
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| 106 | //Number of steps of a simulator for one step of Kalman |
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| 107 | for ( int ii=0; ii<Nsimstep;ii++ ) { |
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| 108 | //simulator |
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| 109 | sim_profile_vec01t ( Ww,vecW ); |
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| 110 | pmsmsim_step ( Ww ); |
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| 111 | }; |
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| 112 | ut ( 0 ) = KalmanObs[4]; |
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| 113 | ut ( 1 ) = KalmanObs[5]; |
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[744] | 114 | xt = fxu->eval ( xtm,ut ) + diag ( sqrt ( Qdiag ) ) *randn ( 4 ); |
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| 115 | dt = hxu->eval ( xt,ut ) + diag(sqrt(Rdiag))*randn(2); |
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[281] | 116 | xtm = xt; |
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| 117 | |
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| 118 | //Variances |
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| 119 | if ( tK==1000 ) Qdiag ( 0 ) *=10; |
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| 120 | if ( tK==2000 ) Qdiag ( 0 ) /=10; |
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| 121 | if ( tK==3000 ) Qdiag ( 1 ) *=10; |
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| 122 | if ( tK==4000 ) Qdiag ( 1 ) /=10; |
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| 123 | if ( tK==5000 ) Qdiag ( 2 ) *=10; |
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| 124 | if ( tK==6000 ) Qdiag ( 2 ) /=10; |
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| 125 | if ( tK==7000 ) Qdiag ( 3 ) *=10; |
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| 126 | if ( tK==8000 ) Qdiag ( 3 ) /=10; |
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| 127 | |
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| 128 | //estimator |
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[744] | 129 | KFE.bayes ( dt,ut ); |
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| 130 | M.bayes ( dt,ut ); |
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[281] | 131 | |
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[744] | 132 | L->log_vector ( l_X,xt ); |
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| 133 | L->log_vector ( l_D,concat ( dt,ut ) ); |
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| 134 | L->log_vector ( l_Q,Qdiag ); |
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[285] | 135 | |
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[744] | 136 | KFE.log_write ( ); |
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| 137 | M.log_write ( ); |
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[281] | 138 | L->step(); |
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| 139 | } |
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| 140 | L->finalize(); |
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| 141 | //Exit program: |
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| 142 | |
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| 143 | return 0; |
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| 144 | } |
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