1 | /*! |
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2 | \file |
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3 | \brief DataSource for experiments with realistic simulator of the PMSM model |
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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 <stat/loggers.h> |
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14 | #include "simulator.h" |
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15 | #include "pmsm.h" |
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16 | |
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17 | //! Simulator of PMSM machine with predefined profile on omega |
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18 | class pmsmDS : public DS { |
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19 | |
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20 | protected: |
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21 | //! indeces of logged variables |
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22 | int L_x, L_ou, L_oy, L_iu, L_optu; |
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23 | //! Setpoints of omega in timespans given by dt_prof |
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24 | vec profileWw; |
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25 | //! time-step for profiles |
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26 | double dt_prof; |
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27 | //! Number of miliseconds per discrete time step |
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28 | int Dt; |
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29 | //! options for logging, - log predictions of 'true' voltage |
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30 | bool opt_modu; |
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31 | public: |
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32 | //! Constructor with fixed sampling period |
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33 | pmsmDS () {Dt=125; Drv=RV ( "{o_ua o_ub o_ia o_ib t_ua t_ub }" );} |
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34 | void set_parameters ( double Rs0, double Ls0, double Fmag0, double Bf0, double p0, double kp0, double J0, double Uc0, double DT0, double dt0 ) { |
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35 | pmsmsim_set_parameters ( Rs0, Ls0, Fmag0, Bf0, p0, kp0, J0, Uc0, DT0, dt0 ); |
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36 | } |
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37 | //! parse options: "modelu" => opt_modu=true; |
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38 | void set_options ( string &opt ) { |
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39 | opt_modu = ( opt.find ( "modelu" ) ==string::npos ); |
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40 | } |
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41 | void getdata ( vec &dt ) {dt=vec ( KalmanObs,6 );} |
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42 | void write ( vec &ut ) {} |
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43 | |
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44 | void step() { |
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45 | static int ind=0; |
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46 | static double dW; // increase of W |
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47 | static double Ww; // W |
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48 | if ( t>=dt_prof*ind ) { |
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49 | ind++; |
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50 | if ( ind<profileWw.length() ) { |
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51 | //linear increase |
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52 | if ( profileWw.length() ==1 ) { |
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53 | Ww=profileWw ( 0 ); dW=0.0; |
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54 | } |
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55 | else { |
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56 | dW = profileWw ( ind )-profileWw ( ind-1 ); |
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57 | dW *=125e-6/dt_prof; |
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58 | } |
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59 | } |
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60 | else { |
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61 | dW = 0; |
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62 | } |
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63 | } |
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64 | Ww += dW; |
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65 | //Simulate Dt seconds! |
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66 | for ( int i=0;i<Dt;i++ ) { pmsmsim_step ( Ww );} |
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67 | }; |
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68 | |
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69 | void log_add ( logger &L ) { |
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70 | L_x = L.add ( rx, "x" ); |
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71 | L_oy = L.add ( ry, "o" ); |
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72 | L_ou = L.add ( ru, "o" ); |
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73 | L_iu = L.add ( ru, "t" ); |
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74 | // log differences |
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75 | if ( opt_modu ) { |
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76 | L_optu = L.add ( ru, "model" ); |
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77 | } |
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78 | } |
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79 | |
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80 | void logit ( logger &L ) { |
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81 | L.logit ( L_x, vec ( x,4 ) ); |
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82 | L.logit ( L_oy, vec_2 ( KalmanObs[2],KalmanObs[3] ) ); |
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83 | L.logit ( L_ou, vec_2 ( KalmanObs[0],KalmanObs[1] ) ); |
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84 | L.logit ( L_iu, vec_2 ( KalmanObs[4],KalmanObs[5] ) ); |
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85 | if ( opt_modu ) { |
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86 | double sq3=sqrt ( 3.0 ); |
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87 | double ua,ub; |
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88 | double i1=x[0]; |
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89 | double i2=0.5* ( -i1+sq3*x[1] ); |
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90 | double i3=0.5* ( -i1-sq3*x[1] ); |
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91 | double u1=KalmanObs[0]; |
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92 | double u2=0.5* ( -u1+sq3*KalmanObs[1] ); |
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93 | double u3=0.5* ( -u1-sq3*KalmanObs[1] ); |
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94 | |
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95 | double du1=0.7* ( double ( i1>0.1 ) - double ( i1<-0.1 ) ) +0.05*i1; |
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96 | double du2=0.7* ( double ( i2>0.1 ) - double ( i2<-0.1 ) ) +0.05*i2; |
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97 | double du3=0.7* ( double ( i3>0.1 ) - double ( i3<-0.1 ) ) +0.05*i3; |
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98 | ua = ( 2.0* ( u1-du1 )- ( u2-du2 )- ( u3-du3 ) ) /3.0; |
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99 | ub = ( ( u2-du2 )- ( u3-du3 ) ) /sq3; |
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100 | L.logit ( L_optu , vec_2 ( ua,ub ) ); |
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101 | } |
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102 | } |
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103 | |
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104 | void set_profile ( double dt, const vec &Ww ) {dt_prof=dt; profileWw=Ww;} |
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105 | }; |
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