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

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12 Controller evaluation 118 a good indication for it. The sensor inputs and other settings are taken from the Heat-Up simulation test case, presented later in this chapter in Section 12.4. All estimated values are set to zero. The optimum has been found by a long-time simulation 2 . After 100 seconds, the ambient temperature is lowered by 5 degrees to show how the optimiser reacts a (sudden) change. �� Figure 12.2: Convergence plot of optimisation runs with a static problem. The result of 10 test-runs is shown in Figure 12.2. The stochastic nature of the evolutionary algorithm is seen by the fact that all solutions are different. Therefore, the mean value and the standard deviation are introduced to show the average algorithm results. The fitness of the best possible solution changes with the environment as it can be observed at the change of the ambient temperature. The optimum is not reached by any of the lines, but all of the results converge to it. This happens quite fast in the beginning. After 10 seconds (5 optimiser runs) the mean only is 10 points away from the best possible fitness. This equals approximately 4% of the worst possible score . After the sudden change at , the peak is even smaller and 10 seconds later the mean only is 4 points away from the optimum. This small difference will usually not 2The optimiser will always converge to the optimum, if one can be found, after an infinite time due to its heuristic nature.
12 Controller evaluation 119 be noticed by the passenger. The results show that the optimiser algorithm is able to come close to the optimum in 10-20 seconds in the beginning. Even a sudden and comparable big change of 5 degrees Celsius is tracked quite fast. Other conditions (different temperatures and settings) showed comparable results. This proofs that the developed algorithm is suitable for the car climate control task. The population size was set to 10, the number of children was 20 and 2 generations were simulated per run, which has a sample time of 2 seconds. A faster convergence can be achieved with higher values, but it requires more resources. The effect of an evaluation of 10 generations is also shown in Figure 12.2. However, these better results are only obtained with a more than five times higher computational effort (cf. Section 8.6.2). One has to keep in mind that the results above have been obtained without any parameter optimisation. The adaptive concept, which includes the base population and the random immigrants, is a trade-off between the ability to track a changing environment and convergence speed which can be adjusted, for example by changing the number of elite individuals. Even faster reactions may be achieved by adapting the number of generations, children and the population size to fully use the available computing power. 12.3 Simulation test layout The ability of the complete controller to reach the objectives that it was designed for is examined in a computer based simulation using Matlab Simulink. Four models are used for this purpose. The first is the controller, presented in Chapter 11. The same program as in the car is used, which is possible through its design in Matlab Simulink. It is the big yellow block on the left side in the simulation model shown in Figure 12.3. The input values to the control block are filtered by a sensor simulation (orange, to the left of the controller). Here, the approximated characteristics from the data sheet are implemented for each sensor. The temperature sensors are quite slowly reacting and have a noise in the magnitude up to one degree Celsius. The humidity sensors are faster and also have lower noise. The outputs of the controller are fed into a model of the HVAC-unit electrics and
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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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Page 87 and 88: 6 The optimiser: an evolutionary al
Page 89 and 90: 7 The HVAC-system controller 69 �
Page 91 and 92: 7 The HVAC-system controller 71 7.5
Page 93 and 94: 8 System and functionality integrat
Page 115 and 116: 9 The MUTE project 95 order to fit
Page 117 and 118: 10 Hardware and characteristics of
Page 129 and 130: 11 Matlab implementation The climat
Page 131 and 132: 11 Matlab implementation 111 Only r
Page 133 and 134: 11 Matlab implementation 113 left i
Page 135 and 136: 11 Matlab implementation 115 tion a
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Page 141 and 142: 12 Controller evaluation 121 Number
Page 143 and 144: 12 Controller evaluation 123 Table
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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
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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 and 180: Optimiser code 159 230 ∗/ ∗ ===
Page 181 and 182: Optimiser code 161 ∗ IMEA_breedin
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
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Optimiser code 169 690 ∗ Latest R
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