The Development of Neural Network Based System Identification ...

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2 CHAPTER 1 INTRODUCTION Surveillance Search & Rescue Rotorcraft Based UAV Applications Law Enforcement Inspection Aerial Mapping Cinematography Figure 1.1 Examples of typical applications of helicopter based UAS. single rotor helicopter configuration offers numerous potential capabilities as shown in Figure 1.1. Potential applications include surveillance, aerial mapping, cinematography, high structure inspection and monitoring operations. Furthermore, the helicopters are also known to be indispensable air vehicles for finding and rescuing stranded individuals or transporting accident victims. Police departments use them for traffic control, ground support, high-speed car pursuits, observation, air patrol and control of large-scale public events or public order incidents. Fire fighters use helicopters for precise delivery of fire extinguishing chemicals to forest fires. More and more electric power companies are using helicopters to inspect towers and transmission lines for corrosion and other defects and to subsequently make repairs. All of these applications require low altitude flight capability to hover over targets and demand dangerous close proximity flight patterns. Any fatal error presents immense risks to human pilot safety. Since the rotorcraft based UAS possesses unique manoeuvre capabilities to hover and cruising at a lower speed, rotorcraft UAS are deemed more suitable for these kinds of application compared to fixed wing UAS. An unmanned helicopter system with built-in autonomous capability can eliminate unnecessary risks
1.2 PROBLEM STATEMENT 3 to the human pilot and will increase the effectiveness of the helicopter operation. Furthermore, the helicopter based UAS is also capable to take-off and land vertically from limited space, hence making it much more efficient than fixed wing UAS that usually requires a runway for take-off and landing. Automation of these specific missions requires development of an automatic flight control system (AFCS) for helicopter based UAS. The automatic control function is responsible for manipulating the inputs to a dynamical system to obtain a desired effect on its outputs without human intervention in the control loop. This is achieved using computation of control laws that calculate input commands for vehicle actuators (i.e. rotor cyclic input, aileron, elevator etc.) to produce torques and forces acting on the vehicle in controlling its 6 degrees of freedom (DOF) motion (position, orientation, and their time derivatives). The work presented in this thesis will focus on the development of a flight control system for a miniature helicopter UAS in hovering mode, which is necessary for aerial surveillance and monitoring operations. 1.2 PROBLEM STATEMENT A miniature single rotor UAS is regarded as an inherently unstable non-linear system with fast responsive dynamics due to their small size. The research communities in industries and universities use commercially available model scaled helicopters as experimental and development airframes for Guidance, Navigation and Control (GNC) research due to airframes’ sufficient payload capability. Due to the helicopter’s unstable dynamics nature, low level controls such as velocity and attitude feedback controllers are required to stabilise the aircraft’s 3 DOF motion. If the helicopter UAS fails to receive stabilising control commands for even a brief period, it will most likely become unstable and crash. This makes conducting research on a helicopter platform challenging where there is no room for error or the consequence can be disastrous. The AFCS for helicopter based UAS was traditionally designed using the linearisation principle of rigid body equation of motion at various conditions throughout the flight envelope [Mettler, 2003, Kendoul, 2012]. Subsequently, the controller system
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Page 29 and 30: NOMENCLATURE AFCS Automatic Flight
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2.5 SUMMARY 53 design based on the
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56 CHAPTER 3 AERIAL PLATFORM AND CU
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Chapter 4 NEURAL NETWORK BASED SYST
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4.2 THE ARTIFICIAL NEURAL NETWORKS
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4.3 SYSTEM IDENTIFICATION WITH NEUR
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4.4 SUMMARY 117 Segment 1 Segment 2
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Chapter 5 NN BASED SYSTEM IDENTIFIC
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5.2 OFF-LINE BASED SYSTEM IDENTIFIC
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5.6 ON-LINE SYSTEM IDENTIFICATION 1
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5.6 ON-LINE SYSTEM IDENTIFICATION 1
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5.7 MODEL PERFORMANCE COMPARISON US
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5.8 SUMMARY 151 methods such as wei
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5.8 SUMMARY 153 would enable the im
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156 CHAPTER 6 NEURAL NETWORK BASED
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Chapter 7 FLIGHT CONTROL SYSTEM DES
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7.2 FLIGHT TESTS IMPLEMENTATION 185
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7.3 EXPERIMENTAL RESULTS 187 1 0.8
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7.3 EXPERIMENTAL RESULTS 189 Longit
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7.3 EXPERIMENTAL RESULTS 191 Pitch
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7.3 EXPERIMENTAL RESULTS 193 Flight
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7.3 EXPERIMENTAL RESULTS 195 140 Ya
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7.3 EXPERIMENTAL RESULTS 197 indica
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7.3 EXPERIMENTAL RESULTS 199 20 100
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7.3 EXPERIMENTAL RESULTS 201 Yaw Ra
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7.3 EXPERIMENTAL RESULTS 203 mainta
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7.3 EXPERIMENTAL RESULTS 205 Yaw Ra
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7.4 SUMMARY 207 data measured from
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210 CHAPTER 8 CONCLUSIONS AND FUTUR
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212 CHAPTER 8 CONCLUSIONS AND FUTUR
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214 REFERENCES B. W. Bequette. Non-
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216 REFERENCES Konstantinos Dalamag
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218 REFERENCES Wayne Johnson. Helic
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220 REFERENCES B. Mettler, Mark B.
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222 REFERENCES V. R. Puttige and S.
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224 REFERENCES D.H. Shim. Hierarchi
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226 REFERENCES Nikos Vitzilaios and
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