| 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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| 15 | #include <estim/libPF.h> |
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| 16 | #include <estim/ekf_templ.h> |
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| 17 | #include <stat/libFN.h> |
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| 18 | |
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| 19 | #include <stat/loggers_ui.h> |
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| 20 | |
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| 21 | #include "../pmsm.h" |
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| 22 | #include "simulator.h" |
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| 23 | #include "../sim_profiles.h" |
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| 24 | |
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| 25 | using namespace bdm; |
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| 26 | |
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| 27 | int main ( int argc, char* argv[] ) { |
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| 28 | const char *fname; |
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| 29 | if ( argc>1 ) {fname = argv[1]; } |
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| 30 | else { fname = "unitsteps.cfg"; } |
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| 31 | UIFile F ( fname ); |
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| 32 | |
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| 33 | int Ndat; |
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| 34 | int Npart; |
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| 35 | double h = 1e-6; |
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| 36 | int Nsimstep = 125; |
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| 37 | |
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| 38 | vec Qdiag; |
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| 39 | vec Rdiag; |
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| 40 | try { |
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| 41 | // Kalman filter |
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| 42 | F.lookupValue ( "ndat", Ndat ); |
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| 43 | F.lookupValue ( "Npart",Npart ); |
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| 44 | |
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| 45 | Qdiag= getvec ( F.lookup ( "dQ" ) ); //( "1e-6 1e-6 0.001 0.0001" ); //zdenek: 0.01 0.01 0.0001 0.0001 |
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| 46 | Rdiag=getvec ( F.lookup ( "dR" ) );// ( "1e-8 1e-8" ); //var(diff(xth)) = "0.034 0.034" |
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| 47 | } |
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| 48 | catch UICATCH; |
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| 49 | // internal model |
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| 50 | |
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| 51 | IMpmsm fxu; |
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| 52 | // Rs Ls dt Fmag(Ypm) kp p J Bf(Mz) |
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| 53 | fxu.set_parameters ( 0.28, 0.003465, Nsimstep*h, 0.1989, 1.5 ,4.0, 0.04, 0.0 ); |
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| 54 | // observation model |
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| 55 | OMpmsm hxu; |
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| 56 | |
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| 57 | vec mu0= "0.0 0.0 0.0 0.0"; |
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| 58 | chmat Q ( Qdiag ); |
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| 59 | chmat R ( Rdiag ); |
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| 60 | EKFCh KFE ; |
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| 61 | KFE.set_parameters ( &fxu,&hxu,Q,R ); |
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| 62 | KFE.set_est ( mu0, chmat ( zeros ( 4 ) ) ); |
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| 63 | KFE.set_rv(rx); |
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| 64 | |
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| 65 | RV rQ ( "{Q }","4" ); |
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| 66 | EKFCh_unQ KFEp ; |
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| 67 | KFEp.set_parameters ( &fxu,&hxu,Q,R ); |
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| 68 | KFEp.set_est ( mu0, chmat ( zeros ( 4 ) ) ); |
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| 69 | |
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| 70 | migamma_fix evolQ ; |
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| 71 | MPF<EKFCh_unQ> M; |
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| 72 | M.set_parameters( &evolQ,&evolQ,Npart); |
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| 73 | // initialize |
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| 74 | evolQ.set_parameters ( 0.1, 10*Qdiag, 1.0 ); //sigma = 1/10 mu |
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| 75 | evolQ.condition ( 10*Qdiag ); //Zdenek default |
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| 76 | M.set_statistics ( evolQ._e() , &KFEp ); |
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| 77 | evolQ.set_parameters ( 0.10, 10*Qdiag,0.999 ); //sigma = 1/10 mu |
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| 78 | // |
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| 79 | |
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| 80 | M.set_rv(concat(rQ,rx)); |
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| 81 | |
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| 82 | dirfilelog *L; UIbuild(F.lookup("logger"), L);// ( "exp/mpf_test",100 ); |
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| 83 | int l_X = L->add ( rx, "xt" ); |
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| 84 | int l_D = L->add ( concat ( ry,ru ), "" ); |
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| 85 | int l_Q= L->add ( rQ, "" ); |
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| 86 | |
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| 87 | KFE.set_options("logbounds"); |
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| 88 | KFE.log_add(L,"KF"); |
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| 89 | M.set_options("logbounds"); |
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| 90 | M.log_add(L,"M"); |
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| 91 | L->init(); |
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| 92 | |
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| 93 | // SET SIMULATOR |
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| 94 | pmsmsim_set_parameters ( 0.28,0.003465,0.1989,0.0,4,1.5,0.04, 200., 3e-6, h ); |
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| 95 | vec dt ( 2 ); |
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| 96 | vec ut ( 2 ); |
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| 97 | vec xt ( 4 ); |
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| 98 | vec xtm=zeros ( 4 ); |
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| 99 | double Ww=0.0; |
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| 100 | vec vecW=getvec(F.lookup("profile")); |
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| 101 | |
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| 102 | for ( int tK=1;tK<Ndat;tK++ ) { |
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| 103 | //Number of steps of a simulator for one step of Kalman |
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| 104 | for ( int ii=0; ii<Nsimstep;ii++ ) { |
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| 105 | //simulator |
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| 106 | sim_profile_vec01t ( Ww,vecW ); |
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| 107 | pmsmsim_step ( Ww ); |
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| 108 | }; |
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| 109 | ut ( 0 ) = KalmanObs[4]; |
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| 110 | ut ( 1 ) = KalmanObs[5]; |
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| 111 | xt = fxu.eval ( xtm,ut ) + diag ( sqrt ( Qdiag ) ) *randn ( 4 ); |
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| 112 | dt = hxu.eval ( xt,ut ); |
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| 113 | xtm = xt; |
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| 114 | |
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| 115 | //Variances |
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| 116 | if ( tK==1000 ) Qdiag ( 0 ) *=10; |
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| 117 | if ( tK==2000 ) Qdiag ( 0 ) /=10; |
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| 118 | if ( tK==3000 ) Qdiag ( 1 ) *=10; |
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| 119 | if ( tK==4000 ) Qdiag ( 1 ) /=10; |
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| 120 | if ( tK==5000 ) Qdiag ( 2 ) *=10; |
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| 121 | if ( tK==6000 ) Qdiag ( 2 ) /=10; |
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| 122 | if ( tK==7000 ) Qdiag ( 3 ) *=10; |
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| 123 | if ( tK==8000 ) Qdiag ( 3 ) /=10; |
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| 124 | |
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| 125 | //estimator |
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| 126 | KFE.bayes ( concat ( dt,ut ) ); |
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| 127 | M.bayes ( concat ( dt,ut ) ); |
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| 128 | |
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| 129 | L->logit ( l_X,xt ); |
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| 130 | L->logit ( l_D,concat ( dt,ut ) ); |
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| 131 | L->logit ( l_Q,Qdiag ); |
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| 132 | |
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| 133 | KFE.logit(L); |
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| 134 | M.logit(L); |
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| 135 | L->step(); |
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| 136 | } |
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| 137 | L->finalize(); |
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| 138 | //Exit program: |
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| 139 | |
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| 140 | delete L; |
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| 141 | return 0; |
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| 142 | } |
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