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Automatic Takeoff and Landing of Unmanned Fixed Wing Aircrafts a Systems Engineering Approach

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Automatic Takeoff and Landing of Unmanned Fixed Wing Aircrafts a Systems Engineering Approach Master of Science Thesis in Computer Science Department of Electrical Engineering, Linköping University, 2016 Automatic Takeoff and Landing of Unmanned Fixed Wing Aircrafts A Systems Engineering Approach Magnus Vestergren Master of Science Thesis in Computer Science Automatic Takeoff and Landing of Unmanned Fixed Wing Aircrafts A Systems Engineering Approach Magnus Vestergren LiTH-ISY-EX--16/5009--SE Supervisor: Ola Dahl isy, Linköpings universitet Thom Magnusson Instrument Control Sweden AB Examiner: Tomas Svensson isy, Linköpings universitet Division of Computer Engineering Department of Electrical Engineering Linköping University SE-581 83 Linköping, Sweden Copyright © 2016 Magnus Vestergren Abstract The purpose of this thesis is to extend an existing autopilot with automatic take- off and landing algorithms for small fixed wing unmanned aircrafts. The work has been done from a systems engineering perspective and as for solution candi- dates this thesis has a bias towards solutions utilizing fuzzy logic. The coveted promises of fuzzy logic was primarily the idea to have a design that was easily tunable with very little knowledge beyond flight experience for a particular air- craft. The systems engineering perspective provided a way to structure and reason about the project where the problem has been decoupled from different solutions and the work has been divided in a way that would allow multiple aspects of the project to be pursued simultaneously. Though the fuzzy logic controllers delivered functional solutions the promises related to ease of tuning was not fulfilled in a landing context. This might have been a consequence of the designs attempted but in the end a simpler solution outperformed the implemented fuzzy logic controllers. Takeoff did not present the same issues in tuning but did require some special care to handle the initial low airspeeds in an hand launch. iii Contents List of Figures viii List of Tables x Notation xi 1 Problem description and approach 1 1.1 A systems engineering approach . 2 2 Background - Fuzzy logic 3 2.1 Criticisms of fuzzy logic . 4 2.2 Fuzzy logic in a control system . 5 2.2.1 Fuzzy inference system . 5 2.2.2 Mamdani inference system . 6 2.2.3 Sugeno fuzzy inference system . 8 3 Background - Fixed wing aircrafts 11 3.1 Stall . 11 3.2 Landing a fixed wing UAV . 13 3.3 Takeoff with a fixed wing UAV . 14 3.4 The ground effect . 14 4 Background - Splines 15 5 Desired system properties 19 5.1 Landing . 19 5.2 Takeoff .................................. 19 6 System components 21 6.1 Spy Owl 100 . 21 6.2 Spy Owl 200 . 21 6.2.1 Altimeter . 22 6.3 EasyPilot 3.0 . 23 6.3.1 Sensor unit . 24 v vi Contents 6.3.2 Simulation . 25 6.4 SkyView Ground Control Software . 25 6.5 STANAG 4586 . 25 6.6 External factors . 26 7 Performance 27 7.1 Measurements . 27 7.1.1 Landing . 28 7.1.2 Takeoff .............................. 28 7.2 Autopilot . 29 7.3 Consequences of deviations from the desired behavior . 29 7.3.1 Landing . 29 7.3.2 Takeoff .............................. 29 8 Cost 31 9 Design 33 9.1 Fuzzy logic . 33 9.1.1 Mamdani aggregation . 34 9.1.1.1 Integration . 34 9.1.1.2 Custom . 34 9.1.1.3 Precomputed . 35 9.1.1.4 Aggregation summary . 36 9.2 Landing controller . 36 9.2.1 Fuzzy controller . 36 9.2.2 Flare . 37 9.3 Approach trajectory . 37 9.3.1 Pitch lookahead . 38 9.4 Approach/Descend trajectory . 39 9.5 Keeping the aircraft on track . 40 9.6 Takeoff Controller . 40 9.7 Consequences, difficulties and limitations . 40 9.8 Stall . 41 9.9 Operator . 42 9.9.1 Landing . 42 9.9.2 Takeoff .............................. 42 9.10 Retry and abort conditions . 44 10 Implementation 45 10.1 Fuzzy logic . 45 10.1.1 Membership functions . 45 10.1.2 Aggregation . 46 10.1.2.1 Mamdani custom algorithm . 46 10.1.3 Defuzzification . 48 10.2 Landing controller . 49 10.2.1 Approach controller . 49 10.2.2 Flare . 50 Contents vii 10.3 Approach trajectory . 51 10.4 Lookahead . 51 10.5 Takeoff controller . 52 11 Results 55 11.1 Execution performance . 55 11.1.1 How the memory performance was measured . 55 11.1.2 How the execution performance was measured . 56 11.1.3 Execution performance measurements . 56 11.2 Flight performance . 57 11.2.1 How the performance was measured . 57 11.2.2 Landing simulation results . 58 11.2.3 Landing real world results . 59 11.2.4 Takeoff simulation results . 63 11.2.5 Takeoff real world results . 65 11.3 Trade-off requirements . 65 12 Conclusions 69 12.1 Landing . 70 12.2 Takeoff .................................. 70 12.3 Fuzzy logic . 70 12.4 Systems engineering . 71 12.5 Future work . 71 Bibliography 73 List of Figures 2.1 Potential membership function for the class of tall men. 4 2.2 Common steps in a fuzzy inference system. 6 2.3 The steps and their relations in a Mamdani type fuzzy inference system. 7 2.4 The aggregation step in a Mamdani fuzzy inference system illus- trated with the max aggregation method. 8 2.5 Mamdani defuzzifcation example, taking the center of gravity on the x-axis of the fuzzy set to produce an output. 9 3.1 A typical relation between coefficient of lift and angle of attack, illustrating the effects of a stall [13]. The critical angle of attack, in this case, is about 17◦........................... 12 3.2 Illustration of the airflow on an airfoil in normal and stall condi- tions [14]. 12 4.1 Blending functions for a Bézier curve with four control points. 16 4.2 Blending functions for the Catmull-Rom spline. 17 6.1 Spy Owl 100................................ 22 6.2 Spy Owl 200................................ 23 6.3 EasyPilot block diagram from the viewpoint of this thesis. 24 6.4 EasyPilot 3.0. .............................. 24 6.5 User interface of SkyView GCS. 26 9.1 System design block diagram. 34 9.2 Rough outline of a basic approach trajectory. 39 9.3 Landing route as presented in SkyView GCS. 43 10.1 The triangle and trapezoid membership functions. 46 10.2 Illustration of the aggregation step using the max aggregation method. 47 10.3 The result of the custom aggregation method. 48 10.4 Creating a segment from two points and dividing the segment into a triangle and a rectangle. 48 10.5 Illustration of of the segments needed to consider during defuzzifi- cation after aggregation with integration and the custom aggrega- tion method. 49 viii LIST OF FIGURES ix 10.6 Illustrating the lookahead algorithm. 52 11.1 Simulated landing with fuzzy logic controller regulating the throt- tle and pitch. 58 11.2 Simulated landing with fuzzy logic controller regulating the throt- tle. 59 11.3 Simulated landing with the lookahead algorithm. 60 11.4 Spy Owl 100 landing with fuzzy logic controller regulating the throttle and pitch. 61 11.5 Spy Owl 100 landing with the lookahead controller. 62 11.6 Spy Owl 200 landing with the lookahead controller. 63 11.7 Horizontal distance (left/right from the UAVs point of view) of the Spy Owl 100 from the desired landing trajectory. 64 11.8 Takeoff data from a hand launch with Spy Owl 100. 66 11.9 Takeoff data from a hand launch with Spy Owl 200. 66 List of Tables 6.1 Spy Owl 100 Specifications. 22 6.2 Spy Owl 200 Specifications. 23 6.3 Fluke 414D with Porcupine LR4 interface board specifications. 23 9.1 Memory consumption, in number of values, required for the pre- computed values. 36 9.2 Abort conditions and possible actions. 44 10.1 Landing fuzzy rule set: Regulate altitude with the throttle and air- speed with the desired pitch. 50 10.2 Landing fuzzy rule set: Regulate throttle after altitude and keep a fixed pitch. 50 10.3 Fuzzy rule set abbreviations and descriptions. 50 10.4 Takeoff fuzzy rule set: Simple rule set where the input is the air- speed of the aircraft output is the desired pitch level. 52 11.1 Memory consumption for the fuzzy logic and lookahead controllers. 57 11.2 Time to perform one iteration of the fuzzy logic and lookahead controllers. 57 x Notation Glossary and acronyms Term Explanation AGL Altitude Over Ground. Ailerons Control surface to control the roll of an.
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