[223] | 1 | /*! |
---|
[215] | 2 | \file |
---|
[223] | 3 | \brief Simulation of disturbances in PMSM model, PWM and integration disturbances are distinguished |
---|
[215] | 4 | \author Vaclav Smidl. |
---|
| 5 | |
---|
| 6 | ----------------------------------- |
---|
| 7 | BDM++ - C++ library for Bayesian Decision Making under Uncertainty |
---|
| 8 | |
---|
| 9 | Using IT++ for numerical operations |
---|
| 10 | ----------------------------------- |
---|
| 11 | */ |
---|
| 12 | |
---|
| 13 | #include <itpp/itbase.h> |
---|
| 14 | #include <estim/libKF.h> |
---|
| 15 | #include <estim/libPF.h> |
---|
| 16 | #include <stat/libFN.h> |
---|
| 17 | |
---|
| 18 | #include <stat/loggers.h> |
---|
| 19 | |
---|
| 20 | #include "pmsm.h" |
---|
| 21 | #include "simulator.h" |
---|
| 22 | #include "sim_profiles.h" |
---|
| 23 | |
---|
| 24 | using namespace itpp; |
---|
| 25 | int main() { |
---|
| 26 | // Kalman filter |
---|
| 27 | int Ndat = 9000; |
---|
| 28 | double h = 1e-6; |
---|
| 29 | int Nsimstep = 125; |
---|
| 30 | |
---|
[218] | 31 | dirfilelog L("exp/sim",Ndat); |
---|
[215] | 32 | |
---|
| 33 | // internal model |
---|
| 34 | IMpmsm fxu; |
---|
| 35 | // Rs Ls dt Fmag(Ypm) kp p J Bf(Mz) |
---|
| 36 | fxu.set_parameters ( 0.28, 0.003465, Nsimstep*h, 0.1989, 1.5 ,4.0, 0.04, 0.0 ); |
---|
| 37 | // observation model |
---|
| 38 | OMpmsm hxu; |
---|
| 39 | |
---|
| 40 | vec mu0= "0.0 0.0 0.0 0.0"; |
---|
| 41 | // vec Qdiag ( "0.01 0.01 0.01 0.0001" ); //zdenek: 0.01 0.01 0.0001 0.0001 |
---|
| 42 | vec Qdiag ( "0.07 0.056 0.0007 0.0007" ); //zdenek: 0.01 0.01 0.0001 0.0001 |
---|
| 43 | vec Rdiag ( "0.005 0.005" ); //var(diff(xth)) = "0.034 0.034" |
---|
| 44 | EKFfull KFE ( rx,ry,ru ); |
---|
| 45 | KFE.set_est ( mu0, diag ( vec("1 1 1 3.1415") ) ); |
---|
| 46 | KFE.set_parameters ( &fxu,&hxu,diag(Qdiag),diag(Rdiag)); |
---|
| 47 | |
---|
| 48 | |
---|
| 49 | int X_log = L.add(rx,"X"); |
---|
[217] | 50 | int Xp_log = L.add(rx,"Xp"); |
---|
| 51 | int Xp2_log = L.add(rx,"Xp2"); |
---|
[215] | 52 | int E_log = L.add(rx,"E"); |
---|
| 53 | int V_log = L.add(rx,"V"); |
---|
| 54 | int U_log = L.add(ru,"U"); |
---|
[217] | 55 | int U2_log = L.add(ru,"U2"); |
---|
[218] | 56 | int R_log = L.add(RV("{_ }","16"),"R"); |
---|
| 57 | int Ww_log = L.add(RV("{_ }","1"),"W"); |
---|
| 58 | int R2_log = L.add(RV("{_ }","16"),"R2"); |
---|
[215] | 59 | // int O_log = L.add(RV("{_ }","16"),"O"); |
---|
| 60 | L.init(); |
---|
| 61 | |
---|
| 62 | // SET SIMULATOR |
---|
[217] | 63 | //pmsmsim_set_parameters ( 0.28,0.003465,0.1989,0.0,4,1.5,0.04, 200., 3e-6, h ); |
---|
[218] | 64 | pmsmsim_set_parameters ( 0.28,0.003465,0.1989,0.0,4,1.5,0.04, 200., 6e-6, h ); |
---|
[215] | 65 | double Ww=0.0; |
---|
| 66 | vec dt ( 2 ); |
---|
| 67 | |
---|
| 68 | vec xm = zeros(4); |
---|
| 69 | vec xt = zeros(4); |
---|
| 70 | vec xp = zeros(4); |
---|
[217] | 71 | vec xp2 = zeros(4); |
---|
| 72 | vec xp3 = zeros(4); |
---|
[215] | 73 | vec u=zeros(2); |
---|
[217] | 74 | vec u2=zeros(2); |
---|
[218] | 75 | ldmat R0(eye(4),1e-6*ones(4)); |
---|
| 76 | ldmat R(R0); |
---|
| 77 | ldmat R2(R0); |
---|
| 78 | |
---|
| 79 | double frg=0.9; |
---|
[223] | 80 | // vec vecW="0. 0. 0.2 0.4 0.4 0.2 0.0 -0.4 -0.6 -0.6 -0.4 0.0 0.0"; |
---|
| 81 | vec vecW="1 2 4 9 4 2 0 -4 -9 -16 -4 0 0 0"; |
---|
| 82 | vecW*=10.0; |
---|
| 83 | |
---|
[215] | 84 | for ( int tK=1;tK<Ndat;tK++ ) { |
---|
| 85 | //Number of steps of a simulator for one step of Kalman |
---|
| 86 | for ( int ii=0; ii<Nsimstep;ii++ ) { |
---|
| 87 | //simulator |
---|
| 88 | //sim_profile_steps1(Ww); |
---|
[218] | 89 | sim_profile_vec01t(Ww,vecW); |
---|
[215] | 90 | pmsmsim_step ( Ww ); |
---|
| 91 | }; |
---|
| 92 | |
---|
| 93 | u(0)= KalmanObs[0]; |
---|
| 94 | u(1)= KalmanObs[1]; |
---|
| 95 | dt(0)= KalmanObs[2]; |
---|
| 96 | dt(1)= KalmanObs[3]; |
---|
[217] | 97 | u2(0) = KalmanObs[4]; |
---|
| 98 | u2(1) = KalmanObs[5]; |
---|
[215] | 99 | // Try what our model would predict! |
---|
| 100 | xp=fxu.eval(xm,u); |
---|
[218] | 101 | xp2=fxu.eval(xm,zeros(2)); //ZERO input!!!!!!!! |
---|
| 102 | // xp3=fxu.eval(xm,u2); |
---|
[215] | 103 | |
---|
[217] | 104 | // KFE.bayes(concat(dt,u)); |
---|
[215] | 105 | // This is simulator prediction |
---|
| 106 | xt=vec(x,4); //vec from C-array |
---|
| 107 | //Covariance |
---|
[218] | 108 | R*=frg; |
---|
| 109 | R.add(R0,1-frg); |
---|
| 110 | R.opupdt(xt-xp,1.0); |
---|
| 111 | R2*=frg; |
---|
| 112 | R2.add(R0,1-frg); |
---|
| 113 | R2.opupdt(xt-xp2,1.0); |
---|
[215] | 114 | xm = xt; |
---|
| 115 | L.logit(X_log, xt ); |
---|
[217] | 116 | L.logit(Xp_log, xp ); |
---|
| 117 | L.logit(Xp2_log, xp2 ); |
---|
[215] | 118 | L.logit(U_log, u ); |
---|
[217] | 119 | L.logit(U2_log, u2 ); |
---|
[218] | 120 | L.logit(Ww_log, vec_1(Ww)); |
---|
| 121 | L.logit(R_log, vec(R.to_mat()._data(), 16 )); //3.33=1/(1-0.7) |
---|
| 122 | L.logit(R2_log, vec(R2.to_mat()._data(), 16 )); |
---|
[217] | 123 | // L.logit(E_log, KFE._e()->mean() ); |
---|
[215] | 124 | // L.logit(O_log, vec(iCh._data(),16)); //3.33=1/(1-0.7) |
---|
| 125 | // L.logit(Efix_log, KFEep.mean() ); |
---|
| 126 | // L.logit(M_log, Mep.mean() ); |
---|
| 127 | |
---|
| 128 | L.step(); |
---|
| 129 | } |
---|
| 130 | |
---|
| 131 | L.finalize(); |
---|
[218] | 132 | L.itsave("sim.it"); |
---|
[215] | 133 | return 0; |
---|
| 134 | } |
---|