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

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6 The optimiser: an evolutionary algorithm approach 54 Table 6.1: Terms and definitions used for evolutionary algorithms. Design variable parameter which is modified in order to find the optimum Individual complete set of design variables for an optimisation problem Population group of individuals Parents individuals chosen for the generation of children Children new generated individuals through recombination of parents, and mutation Generation the process of parent selection and children creation Fitness figure to compare which individual is closer to the optimum Objective (func.) function that the optimiser seeks to minimise Design space � �� -dimensional search region (� �� is the number of design variables) Constraint technical or physical limit to the design space Pareto front all individuals where the improvement of one objective would lead to the impairment of another Because of the population approach with many individuals at a time there is not only one solution to choose the control signal from, but several. This is advantageous if there are criteria that cannot (or should not) be integrated into the optimisation. Examples of evolutionary algorithms in control systems engineering are found in a survey by Fleming and Purshouse [32]. In general, they report the application for control design, but they also mention some online applications. For a heating system, a PI- controller was online tuned with an EA (Ahmad, Zhang, & Readle, 1997)[33]. In another example the temperature profile of an ethanol fermentation process was optimised online with an EA by Moriyama and Shimizu (1996) [32]. Optimal control algorithms of this type have already been used in climatisation. In a building, Nassif et al. [28] used a NSGA-II algorithm for set-point optimisation of a multi-zone HVAC system with regard to comfort and energy demand in a university building.
6 The optimiser: an evolutionary algorithm approach 55 6.3 Design of an incremental micro evolutionary algorithm While approaches of using evolutionary algorithms in online control have already been made, the circumstances in these cases are not comparable to the ones in car climate control. In the case of the building climate control [28] the population size was set to 100 and 500 generations were evaluated, which lead to a lot of function evaluations. This is possible in a building where more computing power is available, but in a car it is not (yet). The second example close to the planned control layout is the set-point optimisation for an ethanol fermentation process [32]. Here however, the sample time was thirty minutes. For an climate control application it should be not more than several seconds. Therefore, an adapted algorithm is required. 6.3.1 Overview of IMEA In the design of the evolutionary algorithm for the car climate control optimisation par- ticular attention is paid to the computation demand. In comparison to usual applications of EAs the simulated system is through the applied simplifications quite small and in the range of a simple MPC-problem. Thus, the computational effort of the algorithm itself cannot be neglected, as it is often done [34]. The main ideas of the developed incremental micro evolutionary algorithm are: • Use a small population in order to reduce computing time. • Have a fixed amount of function evaluations to give a deterministic run time. • Ensure feasible solutions through a base population inserted at each run. • Adapt mutation to the changes of the environment. • Employ a special ranking scheme to penalise exceeded limits differently. • Augment efficiency through elitism (storing the best individuals for the next run). • Avoid double entries and keep diversity to be able to react to changes.
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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
Page 23 and 24: 1 Introduction 3 adjustment of the
Page 25 and 26: 2 Control objectives 5 2.1 Windscre
Page 27 and 28: 2 Control objectives 7 2.2 Comfort
Page 29 and 30: 2 Control objectives 9 � Metaboli
Page 31 and 32: 2 Control objectives 11 ��
Page 33 and 34: 2 Control objectives 13 Figure 2.6:
Page 35 and 36: 2 Control objectives 15 2.4 Influen
Page 37 and 38: 3 System description 17 or heat com
Page 39 and 40: 3 System description 19 calculated
Page 41 and 42: 3 System description 21 ��
Page 43 and 44: 3 System description 23 � � �
Page 45 and 46: 3 System description 25 defrost tem
Page 47 and 48: 3 System description 27 Figure 3.4:
Page 49 and 50: 3 System description 29 width modul
Page 51 and 52: 3 System description 31 ��
Page 53 and 54: 3 System description 33 shown in Fi
Page 55 and 56: 3 System description 35 of performa
Page 57 and 58: 4 Control strategy The main goal of
Page 59 and 60: 4 Control strategy 39 limits are di
Page 61 and 62: 4 Control strategy 41 ��
Page 63 and 64: 4 Control strategy 43 cabin. This s
Page 65 and 66: 4 Control strategy 45 controlled. T
Page 67 and 68: 5 The disturbance estimator 47 in e
Page 69 and 70: 6 The optimiser: an evolutionary al
Page 73: 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
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10 Hardware and characteristics of
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10 Hardware and characteristics of
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11 Matlab implementation The climat
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11 Matlab implementation 111 Only r
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11 Matlab implementation 113 left i
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11 Matlab implementation 115 tion a
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12 Controller evaluation 117 ��
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12 Controller evaluation 119 be not
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12 Controller evaluation 121 Number
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12 Controller evaluation 123 Table
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12 Controller evaluation 125 In the
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12 Controller evaluation 127 ��
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13 Conclusion 129 13.2 Possible imp
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Bibliography [1] Holger Großmann.
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Bibliography 133 [22] M. Wang, T.M.
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Appendices
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B In- and outputs The interface bet
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Signal Unit Range Type Comment Stat
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In- and outputs 141 B.3 Error codes
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and system parameters 2 concentrati
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Simulation parameters 145 C.2 Contr
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Simulation parameters 147 Panic han
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Simulation parameters 149 C.5 Test
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Additional simulation results 151 D
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Optimiser code 153 real_T ∗panic_
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Optimiser code 155 52 return 10; 54
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Optimiser code 157 138 /∗ return
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Optimiser code 159 230 ∗/ ∗ ===
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Optimiser code 161 ∗ IMEA_breedin
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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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