Design of an Automatic Control Algorithm for Energy-Efficient ...

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276 Optimiser code 160 278 { while (! done) k = 0; 280 l i m j = 0 ; limi = 0; 282 /∗ check if more limits broken by one than by the other individual ∗/ 284 { 286 limi++; 288 limj++; 290 } 292 { while(ordPrio [ prio∗Obj.numObj+k] != 1) if(pop[ i ]. ObjResults [ ordPrio [ prio∗Obj.numObj+k ]] > Obj. limits [ ordPrio [ p r i o ∗Obj . numObj+k ] ] ) if(pop[ j ]. ObjResults [ ordPrio [ prio∗Obj.numObj+k]] > Obj. limits [ ordPrio k++; if(limj > limi) [ p r i o ∗Obj . numObj+k ] ] ) pop[ j ]. ranking++; 294 done = 1; } 296 else if (limj < limi) { 298 pop[ i ]. ranking++; 300 } done = 1; /∗ if no preference found through limits , compare weighting ∗/ 302 if(prio==0 && !done) 304 done = 1; { 306 pop[ j ]. ranking++; 308 pop[ i ]. ranking++; 310 prio ; 312 } 314 } } } } if(pop [ i ] . weightedSum < pop [ j ] . weightedSum) else if (pop [ i ] . weightedSum > pop [ j ] . weightedSum) 316 static void IMEA_breeding( struct individual ∗pop, struct designVariables DV, real_T ∗ fitness , int_T ∗parents , int_T popSize , 318 int_T numChild , real_T max_change) { 320 / ∗ ========================================================================
Optimiser code 161 ∗ IMEA_breeding selects ’numChild ’∗2 parents via global stochastic remainder 322 ∗ selection and creates the children by recombining the parents and mutating 324 ∗/ ∗ ======================================================================== real_T fitSum = 0.0; 326 real_T tmpSum = 0.0; real_T randNr = 0.0; 328 int_T rankMax = 0; 330 332 int_T rankMin = 0; int_T i , j , cnt_parent ; 334 /∗ determine max and min ranking ∗/ 336 { for( i=0;i rankMax) 340 rankMax = pop [ i ] . ranking ; } 342 /∗ assign selection probability ∗/ 344 { if(rankMin == rankMax) /∗avoid problems if all equal ∗/ 346 for( i=0;i
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ISSN 0280-5316 ISRN LUTFD2/TFRT--58
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Diploma thesis subject Problem stat
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Acknowledgements ii Acknowledgement
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Acknowledgements iv 3.1.4 Windscree
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Acknowledgements vi 8.5 System simu
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Nomenclature viii Nomenclature Symb
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Nomenclature x Other symbols Symbol
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Nomenclature xii Subscript Descript
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1 Introduction Energy consumption t
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1 Introduction 3 adjustment of the
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2 Control objectives 5 2.1 Windscre
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2 Control objectives 7 2.2 Comfort
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2 Control objectives 9 � Metaboli
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2 Control objectives 11 ��
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2 Control objectives 13 Figure 2.6:
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2 Control objectives 15 2.4 Influen
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3 System description 17 or heat com
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3 System description 19 calculated
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3 System description 21 ��
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3 System description 23 � � �
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3 System description 25 defrost tem
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3 System description 27 Figure 3.4:
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3 System description 29 width modul
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3 System description 31 ��
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3 System description 33 shown in Fi
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3 System description 35 of performa
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4 Control strategy The main goal of
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4 Control strategy 39 limits are di
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4 Control strategy 41 ��
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4 Control strategy 43 cabin. This s
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4 Control strategy 45 controlled. T
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5 The disturbance estimator 47 in e
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6 The optimiser: an evolutionary al
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7 The HVAC-system controller 69 �
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7 The HVAC-system controller 71 7.5
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8 System and functionality integrat
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9 The MUTE project 95 order to fit
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10 Hardware and characteristics of
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Page 129 and 130: 11 Matlab implementation The climat
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Page 135 and 136: 11 Matlab implementation 115 tion a
Page 137 and 138: 12 Controller evaluation 117 ��
Page 139 and 140: 12 Controller evaluation 119 be not
Page 141 and 142: 12 Controller evaluation 121 Number
Page 143 and 144: 12 Controller evaluation 123 Table
Page 145 and 146: 12 Controller evaluation 125 In the
Page 147 and 148: 12 Controller evaluation 127 ��
Page 149 and 150: 13 Conclusion 129 13.2 Possible imp
Page 151 and 152: Bibliography [1] Holger Großmann.
Page 153 and 154: Bibliography 133 [22] M. Wang, T.M.
Page 155 and 156: Appendices
Page 157 and 158: B In- and outputs The interface bet
Page 159 and 160: Signal Unit Range Type Comment Stat
Page 161 and 162: In- and outputs 141 B.3 Error codes
Page 163 and 164: and system parameters 2 concentrati
Page 165 and 166: Simulation parameters 145 C.2 Contr
Page 167 and 168: Simulation parameters 147 Panic han
Page 169 and 170: Simulation parameters 149 C.5 Test
Page 171 and 172: Additional simulation results 151 D
Page 173 and 174: Optimiser code 153 real_T ∗panic_
Page 175 and 176: Optimiser code 155 52 return 10; 54
Page 177 and 178: Optimiser code 157 138 /∗ return
Page 179: Optimiser code 159 230 ∗/ ∗ ===
Page 183 and 184: Optimiser code 163 414 } 416 } } st
Page 185 and 186: Optimiser code 165 if (sortedPop [
Page 187 and 188: Optimiser code 167 596 ∗ Printout
Page 189: Optimiser code 169 690 ∗ Latest R
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