The Development of Neural Network Based System Identification ...

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212 CHAPTER 8 CONCLUSIONS AND FUTURE WORKS implementation of a full MIMO based controller. One of the primary causes of the computational restriction was due to the Labview R○ plug and play driver for MTi Xsens IMU which was executed together with other time critical processes such as the NN training and NNAPC controller processes. For example, if the user increases the number of prediction or control horizon steps too high, it would significantly impact the IMU measurement loop rate. More computational resources are used to maintain the loop timing of these time critical processes and this significantly reduces the IMU measurement loop rate, thus making the IMU produce chaotic results with measurement error. It is advisable to switch the data handling of the IMU measurement from the sbRIO’s real-time processor to FPGA, in order to reduce the total computation burden of the sbRIO real-time processor. 2. Since the NN model is linearised continuously, the system dynamics can change rapidly at every time instance. For systems with non smooth non-linearity, there will be problems since the extracted linear models are generally valid near the operating point. To counter this problem and to gain a more stable system model, a low-pass filter can be implemented to the A-matrix in the State Space Model as recommended in Witt et al. [2007]. 3. The translational controllers can be designed for the unmanned helicopter system, thus establishing a full six degrees-of-freedom controller. A recommendation for the design of translational control is to use several PID controllers, which will construct set-points for the inner loop roll-pitch MPC controller to drive the helicopter to the required translational positions. 4. To make the unmanned helicopter system ready for free flight, the altitude sensor needs to be replaced. It is recommended to combine two different positional measurement systems such as GPS for tracking translational motion or by using pressure sensor such as a barometer for accurate altitude measurement.
REFERENCES A. Ab Wahab, R. Mamat, and S.S. Shamsudin. The effectiveness of pole placement method in control system design for an autonomous helicopter model in hovering flight. International Journal of Integrated Engineering (Issue on Electrical and Electronic Engineering), 1(3):33–46, 2009. N. Abas, A. Legowo, and R. Akmeliawati. Parameter identification of an autonomous quadrotor. In Mechatronics (ICOM), 2011 4th International Conference On, pages 1–8, May 2011. doi: 10.1109/ICOM.2011.5937198. M. Agarwal. A systematic classification of neural-network-based control. Control Systems, IEEE, 17(2):75–93, 1997. S M Ahmad, M H Shaheed, A J Chipperfield, and M O Tokhi. Non-linear modelling of a one-degree-of-freedom twin-rotor multi-input multi-output system using radial basis function networks. Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering, 216(4):197–208, 2002. URL http://ieeexplore. ieee.org/xpls/abs_all.jsp?arnumber=894926. Zheng A. Allgower, F. Nonlinear model predictive control, volume 26. Birkhauser, Boston, 2000. O. Amidi. An Autonomous Vision Guided Helicopter. PhD thesis, Robotics Institute, Carnegie Mellon University, 1996. O. Amidi, T. Kanade, and K. Fujita. A visual odometer for autonomous helicopter flight. Robotics and Autonomous Systems, 28:185–193, 1999. doi: 10.1016/S0921-8890(99) 00016-0. Duarte Antunes, Carlos Silvestre, and Rita Cunha. On the design of multi-rate tracking controllers: Application to rotorcraft guidance and control. International Journal of Robust and Nonlinear Control, 20(16):1879–1902, 2010. ISSN 1099-1239. doi: 10.1002/rnc.1557. URL http://dx.doi.org/10.1002/rnc.1557. V. S. Asirvadam. Adaptive regularizer for recursive neural network training algorithms. In 11th IEEE International Conferenc Computational Science and Engineering Workshops CSEWORKSHOPS ’08, pages 89–94, 2008. S. N. Balakrishnan and R. D. Weil. Neurocontrol: A literature survey. Mathematical and Computer Modelling, 23(1-2):101–117, 1996. doi: 10.1016/0895-7177(95)00221-9. P. Baldi. Computing with arrays of bell-shaped and sigmoid functions. In Proceedings of the 1990 conference on Advances in neural information processing systems 3, pages 735–742, Denver, CO, 1990. Morgan Kaufmann Publishers Inc.
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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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PUBLICATIONS The following is a lis
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xiv TABLE OF CONTENTS CHAPTER 4 CHA
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LIST OF FIGURES 1.1 Examples of typ
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LIST OF FIGURES xix 3.13 Overview o
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LIST OF FIGURES xxi 5.2 The predict
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LIST OF FIGURES xxiii 5.13 The pred
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LIST OF FIGURES xxv 7.1 The locatio
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LIST OF TABLES 3.1 The specificatio
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NOMENCLATURE AFCS Automatic Flight
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LIST OF TABLES xxxi NNARX QP RBF RC
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2 CHAPTER 1 INTRODUCTION Surveillan
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4 CHAPTER 1 INTRODUCTION is then de
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6 CHAPTER 1 INTRODUCTION the dynami
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8 CHAPTER 1 INTRODUCTION models [Sa
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10 CHAPTER 1 INTRODUCTION improved
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12 CHAPTER 1 INTRODUCTION Chapter 3
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Chapter 2 LITERATURE REVIEW The pur
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2.1 INTRODUCTION 17 • Inefficient
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2.1 INTRODUCTION 19 Figure 2.2 The
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2.2 HELICOPTER DYNAMICS MODELLING A
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2.3 NEURAL NETWORK BASED SYSTEM IDE
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2.4 AUTOMATIC FLIGHT CONTROL SYSTEM
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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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Page 246 and 247: 214 REFERENCES B. W. Bequette. Non-
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