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

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11 Matlab implementation 114 11.3 Programming The controller layout is completely done in Simulink’s graphical programming. The disturbance estimator and the optimiser, as the most complicated parts, are entirely written in C-code. The optimiser code is distributed over five files in order to increase the readabil- ity and re-usability. A chart showing this structure, the containing functions and the dependencies is found in Figure 11.3. �� Figure 11.3: The structure and dependencies of the optimiser C-code. The IMEA optimiser code was kept as general as possible to be able to use it with- out much effort in other projects or even for other purposes. Its main function is called by a Matlab S-function which can, due to its special structure and containing functions, be integrated with a block into Simulink models. This file prepares the input values for the optimiser, adapts the limits and parameters and hands them through pointers onto special structures to the optimiser. The objective function and the system simula-
11 Matlab implementation 115 tion are given by function pointers. This allows easy integration of additional objectives or different system simulations. The objective and system functions are called by the IMEA_evaluateObjective function. The data structures and the complete code of the optimiser is found in Appendix E. The disturbance estimator is integrated directly into one S-function. The carbon dioxide concentration simulation is programmed graphically in Simulink since it only contains standard elements, which allows a faster execution. 11.4 Real-time requirements Matlab Simulink is mainly designed as a simulation tool. However, with special in- and output blocks the models can be compiled with a tool called Real-Time Workshop and used on micro-controllers and other targets. This allow fast testing and implementations for prototypes. The model built for the MUTE car has a sample time of 0.01 seconds. Running all subsystems at this speed would most certainly lead to overruns, i.e. longer calculation times than one sample. Sensor signals are read at a slower speed. Therefore, a suitable sample time has to be found for each system. These can be assigned to every subsystem marked as “atomic” as well as to external S-functions. For the climate controller a general sample time of 0.1 second was chosen. Input values are updated at this frequency via the CAN bus. The optimiser does not need to run that often. Since the timescale of the cabin-system is in the range of several seconds (see Section 3.1.5), a sample time of two seconds was chosen. The speed for the disturbance estimator is also set independently. Here, one second was chosen in order to run slower than the rest but include all new values that are coming from the multiplexer every 0.8 seconds. To connect these systems running at different speeds, rate transition blocks are needed. They assure the data transfer by providing zero-order-hold or a delay functionality depending on the sample time difference.
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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 83 and 84: 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: 11 Matlab implementation 113 left i
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 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
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Optimiser code 165 if (sortedPop [
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Optimiser code 167 596 ∗ Printout
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Optimiser code 169 690 ∗ Latest R
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