The Development of Neural Network Based System Identification ...

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28 CHAPTER 2 LITERATURE REVIEW 1. Dendrites: Accept Inputs 3. Axon: Turn the processed inputs to outputs Direction of signal transmission Nucleus 2. Cell Body/Soma: Process the Inputs 4. Axon terminal buttons: The electrochemical signal transfer point Figure 2.5 A simplified representation of a biological neuron. NN architecture such as Multi-layered Perceptron (MLP) network has been theoretically proven to be an universal approximator, thus capable of approximating any non-linear function of interest to any desired degree of accuracy with sufficiently available neurons [Hornik et al., 1989]. Furthermore, the NN models are also able to establish the required input-output relationship without a priori assumptions about the properties of the data or the governing mathematical models as in first principle modelling [Abas et al., 2011]. The NN modelling approach has been used to perform a diverse range of tasks including prediction, function approximation, pattern classification and clustering. It has been proven in many research investigations and industrial applications as an efficient tool to handle complex input-output mapping. Paliwal and Kumar [2009] carried out a comprehensive literature review of over 100 comparative studies on the ANN and traditional statistical techniques used for prediction and classification tasks in various fields of applications. The review clearly highlights the potential and usefulness of the NN approach to approximate any non-linear mathematical function when it is difficult to handle the task statistically. It can be concluded from the review that NN models outperform the traditional statistical methods in most of the cases or at least performed as well as other statistical methods. The literature also includes numerous applications of NN to address a wide range of
2.3 NEURAL NETWORK BASED SYSTEM IDENTIFICATION 29 complex problems in aeronautical and flight control system applications. For example, the NN approach has been used to identify aerodynamic coefficients of an aircraft and rotorcraft system in Suresh et al. [2003], Dennis and Stengel [1992]. NASA Dryden Flight Research Centre and Boeing company have developed an adaptive flight control system that utilises the NN model to predict the stability and control parameters of the F-15 tactical jet fighter. This prediction data was then continuously used to optimise the control system and to assist the pilot in damage or failure situations of the aircraft [Urnes et al., 2001]. Furthermore, NN is also used to detect faults in the helicopter transmission system and has been proven reliable to classify faults even with different vibration signatures data [Parker et al., 1993]. Review papers commenting on the usage of the NN approach in the aeronautics and flight control system design have been contributed by Calise and Rysdyk [1998] and Faller and Schreck [1996]. There are two main advantages for transitioning neural network technology into UAS: (1) the potential to reduce the flight control system design and development costs, (2) possible reduction in cost associated with the development of large aerodynamic database [Calise and Rysdyk, 1998]. 2.3.1 NN Model Structure Used for Dynamics Modelling There exist several ANN model structures that have been used to model the dynamics representation of a UAV helicopter. The NN model structures differ from each other depending on how their processing units or neurons are interconnected. This section covers the main types of network structures used for modelling the helicopter flight dynamics. Since the helicopter dynamics is non-linear, the NN system identification approach using the NNARX (Neural Network-Auto Regressive structure with eXtra inputs) model structure can be used to address such a problem. Here, the linear model structure such as the ARX model structure was introduced as the basic model structure while the NN network was used to introduce non-linearity to the model estimation. Similar to stability features of the ARX model structure, the prediction from the NNARX model was considered stable since there was only a pure algebraic relationship between
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The Development of Neural Network B
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ABSTRACT This thesis presents the d
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vii the MLP network, the prediction
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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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