The Development of Neural Network Based System Identification ...

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26 CHAPTER 2 LITERATURE REVIEW and δ col pairing). The coupling between collective and pedal channels can be further simplified into two sets of a Single Input-Single Output (SISO) system similar to the assumption used in Mettler [2003] and Samal [2009]. In system identification approach, the non-linear mathematical model from the first principle approach can be identified from the experimental input-output data using error minimisation or optimisation methods. In Kim and Tilbury [2004], a system identification of a model scaled helicopter using time domain analysis tool was presented. The interaction between flybar and the main rotor blade was included in the model development which considers the effects of flybar flapping mechanism on helicopter stability. The identification of the decoupled SISO transfer function was obtained using the least square error minimisation between the time domain response predicted by the transfer function and the measured experiment data. The identification experiment was done in special test benches that restricted the motion of the helicopter to one degree of freedom (DOF). However, results from the time domain system identification indicated that the prediction obtained from the model gave a fairly poor prediction performance and did not track the faster dynamics of the system [Mettler, 2003]. One of the most popular and effective method to identify the dynamics model of small scaled helicopter UAV was based on the identification method proposed by Mettler et al. [2002b]. The method was based on the frequency response identification technique that used analytical software package such as Comprehensive Identification from Frequency Responses (CIFER R○ ) software. This method was mainly used for military fixed wing and rotary wing aircraft system identification [Tischler and Remple, 2006]. Mettler et al. [2002b] carried out a detailed identification work from the collection of flight data and sufficiently estimate linear models in hovering and cruise flight conditions for the Yamaha R-50 helicopter UAV. Both flight conditions were accurately described by the identified linear models with additional dynamic effects such as the first-order rotor and stabiliser bar dynamics with no inflow dynamics effect. The linear models accurately captured the vehicle dynamics roughly around the nominal operating points. Even with the acceptable identification result, Mettler [2003] has further suggested to identify more linear models that represent adequately a large portion of the flight envelope
2.3 NEURAL NETWORK BASED SYSTEM IDENTIFICATION 27 with sufficient accuracy for the control design. This further motivated us to find more comprehensive modelling solutions that cover the extended operating conditions outside the linear range much more effectively. 2.3 NEURAL NETWORK BASED SYSTEM IDENTIFICATION Although various simplified mathematical models have been developed over the years for designing the flight controller for helicopter based UAS, many models suffer performance degradation due to many unmodelled dynamics that are not incorporated in the mathematical model itself. Consider the altitude control channel, significant errors can arise due to simplifying assumptions or omitting several factors such as servo dynamics, rotor speed variation, sensor lag, ground effect, actuator kinematic non-linearities and rotor inflow lag associated with the rate of change of the blade pitch [Garratt and Anavatti, 2012]. These dynamic effects are hard to model exactly, but can be compensated by the use of learning methods such as fuzzy inference system or artificial neural network (ANN) approach. The fuzzy inference system is a modelling approach that represents the input and output mapping through the use of fuzzy set theory. However, the fuzzy modelling approach has a potential limitation that it requires a large amount of data to accurately train the model [Lawrynczuk, 2007a, Kuure-Kinsey et al., 2006b]. The errors from the unmodelled dynamics can be significantly reduced because of the ability of the neural network (NN) to model the complex or near impossible to model dynamic effects without using predefined analytical models. The learning of the complex dynamics mapping can be realised through the learning process from raw flight test data which simplifies the flight controller design significantly. The ANN is a mathematical representation that mimics the biological neurons in the human brain. The typical tasks that are performed by biological neurons are shown in Figure 2.5. Each neuron in the network collects signals from other neurons through its dendrites and in turn sums and processes those messages within its Soma/Cell body. The processed signal is then transferred to the other neurons afterwards through the axon link and terminal buttons. Using a similar approach, the ANN model can be trained to produce solutions to modelling problems through a similar connection mechanism. The
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The Development of Neural Network B
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ABSTRACT This thesis presents the d
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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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5.6 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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