| 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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