1 | /* |
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2 | * nn_esc_1d.cpp |
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3 | * |
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4 | * Created on: Jul 26, 2012 |
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5 | * Author: Berk Calli |
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6 | * Organization: Delft Biorobotics Lab., Delft University of Technology |
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7 | * Contact info: b.calli@tudelft.nl, web: www.dbl.tudelft.nl |
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8 | * |
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9 | * Class for one dimensional neural network extremum seeking control |
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10 | * |
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11 | * * References: |
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12 | * - M. Teixeira and S. Zak, âAnalog neural nonderivative optimizers,â IEEE Transactions on Neural Networks, vol. 9, pp. 629â638, 1998. |
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13 | * - B. Calli, W. Caarls, P. Jonker and M. Wisse, "Comparison of Extremum Seeking Control Algorithms for Robotic Applications", IROS 2012. |
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14 | */ |
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15 | #include "esc_nn/nn_esc_1d.h" |
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16 | |
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17 | NNESC1D::NNESC1D(){ |
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18 | M_ = 0; |
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19 | A_ = 0; |
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20 | ddelta_ = 0; |
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21 | delta_ = 0; |
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22 | B_ = 0; |
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23 | w_switch_old_ = 0; |
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24 | a_switch_old_ = 0; |
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25 | yr_ = 0; |
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26 | period_ = 0; |
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27 | min_peak_ = 0; |
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28 | vel_ref_ = 0; |
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29 | w_switch_ = 0; |
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30 | min_peak_detect_init_ = false; |
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31 | initialized_ = false; |
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32 | |
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33 | } |
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34 | |
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35 | ESC::inputType NNESC1D::getInputType(){ |
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36 | return inputValue; |
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37 | } |
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38 | |
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39 | ESC::outputType NNESC1D::getOutputType(){ |
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40 | return outputVelocity; |
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41 | } |
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42 | |
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43 | std::vector<double> NNESC1D::monitor(){ |
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44 | std::vector<double> monitor_vals; |
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45 | monitor_vals.push_back(yr_); |
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46 | monitor_vals.push_back(min_peak_); |
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47 | monitor_vals.push_back(w_switch_); |
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48 | |
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49 | return monitor_vals; |
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50 | |
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51 | } |
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52 | |
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53 | std::vector<std::string> NNESC1D::monitorNames(){ |
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54 | std::vector<std::string> monitor_names; |
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55 | monitor_names.push_back("driving input value"); |
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56 | monitor_names.push_back("minimum peak detector output"); |
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57 | monitor_names.push_back("w switch value"); |
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58 | |
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59 | return monitor_names; |
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60 | } |
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61 | |
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62 | NNESC1D::NNESC1D(double A,double M, double B, double ddelta, double delta, double period, int stopping_cycle_number, double stoping_min_val){ |
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63 | init(A, M, B, ddelta, delta, period,stopping_cycle_number,stoping_min_val); |
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64 | } |
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65 | |
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66 | void NNESC1D::init(double A, double M, double B, double ddelta, double delta, double period, int stopping_cycle_number, double stoping_min_val){ |
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67 | A_ = A; |
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68 | M_ = M; |
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69 | B_ = B; |
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70 | ddelta_ = ddelta; |
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71 | delta_ = delta; |
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72 | period_ = period; |
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73 | reset(); |
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74 | initialized_ = true; |
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75 | stopping_cycle_number_ = stopping_cycle_number; |
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76 | stoping_min_val_ = stoping_min_val; |
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77 | } |
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78 | |
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79 | std::vector<double> NNESC1D::step(double obj_val){ |
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80 | if (!initialized_){ |
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81 | fprintf(stderr,"The neural network ESC (1D) is not initialized... It will not be executed. \n"); |
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82 | return std::vector<double>(); |
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83 | } |
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84 | |
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85 | if(!min_peak_detect_init_){ |
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86 | yr_ = obj_val; |
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87 | min_peak_detect_init_ = true; |
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88 | obj_val_cycle_init_ = obj_val; |
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89 | } |
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90 | |
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91 | double e = yr_ - obj_val; |
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92 | vel_ref_ = aSwitch(e); |
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93 | min_peak_ = minPeakDetect(-e); |
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94 | w_switch_ = wSwitch(-e); |
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95 | yr_ = yr_ + (w_switch_+min_peak_)*period_; |
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96 | |
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97 | if(vel_ref_old_ != vel_ref_ && vel_ref_ == -A_){ |
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98 | if(obj_val_cycle_init_ - obj_val < stoping_min_val_) |
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99 | nn_cycle_count_++; |
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100 | else |
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101 | nn_cycle_count_ = 0; |
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102 | obj_val_cycle_init_ = obj_val; |
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103 | } |
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104 | |
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105 | vel_ref_old_ = vel_ref_; |
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106 | |
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107 | std::vector<double> output; |
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108 | output.push_back(vel_ref_); |
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109 | return output; |
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110 | } |
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111 | double NNESC1D::wSwitch(double e_minus){ |
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112 | if(e_minus<-delta_){ |
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113 | w_switch_old_ = 0; |
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114 | return 0; |
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115 | } |
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116 | else if(e_minus>delta_){ |
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117 | w_switch_old_ = B_; |
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118 | return B_; |
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119 | } |
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120 | else |
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121 | return w_switch_old_; |
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122 | |
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123 | } |
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124 | |
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125 | double NNESC1D::minPeakDetect(double e_minus){ |
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126 | if(e_minus>0) |
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127 | return 0; |
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128 | else |
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129 | return -M_; |
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130 | } |
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131 | |
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132 | double NNESC1D::aSwitch(double e){ |
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133 | if( e < -ddelta_ ){ |
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134 | a_switch_old_ = -A_; |
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135 | return -A_; |
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136 | } |
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137 | else if(e>=ddelta_){ |
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138 | a_switch_old_ = A_; |
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139 | return A_; |
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140 | } |
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141 | else |
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142 | return a_switch_old_; |
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143 | |
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144 | } |
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145 | |
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146 | void NNESC1D::reset(){ |
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147 | w_switch_old_ = 0; |
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148 | a_switch_old_ = A_; |
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149 | yr_ = 0; |
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150 | min_peak_ = 0; |
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151 | vel_ref_ = 0; |
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152 | w_switch_ = 0; |
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153 | nn_cycle_count_ = 0; |
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154 | vel_ref_old_ = 0; |
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155 | min_peak_detect_init_ = false; |
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156 | |
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157 | } |
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158 | |
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159 | |
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160 | bool NNESC1D::isStoppingConditionsMet(){ |
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161 | if(stopping_cycle_number_ <= 0) |
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162 | return false; |
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163 | else if(nn_cycle_count_ >= stopping_cycle_number_){ |
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164 | return true; |
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165 | } |
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166 | else |
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167 | return false; |
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168 | } |
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