臺灣博碩士論文加值系統

本論文的目的是在探討特徵系統實現理論運用於飛行載具氣動力模式之識別。本文採用線性及非線性之參數系統識別，以求得低速風洞試驗及高速風洞試驗下之載具氣動力參數。
文中設計之線性系統參數識別運用模糊特徵值系統整合風洞試驗系統中之各種變數，包括風洞風速、模型測試攻角、側滑角、水平尾角度、翼型及載具動力(馬達轉速及螺槳型式)等，與測試環境關聯性之相關實用模糊規則定義，進行小型動力無人飛行載具(power-on mini-UAV)及AGARD標準模型進行氣動力參數系統識別。本論文將模擬結果與小型動力無人飛行載具於低速風洞及AGARD標準模型於高速風洞吹試之數據進行比對，證實所提方法之效益。
由於風洞試驗中之各相關變數為高度非線性，故本研究亦運用多層遞迴式神經網路架構結合非線性特徵系統識別方法來探討飛行載具之氣動力參數。本非線性系統最大效益為可以直接引用該構型之線性特徵系統所求得歸屬函數值，定義為非線性之識別系統之初始權重，再利用遞迴式神經網路架構來學習最佳權重值，不需藉實際風洞吹試就能得到近似之飛行載具氣動力參數。
模擬結果初步地證實本線性系統與非線性系統之特徵系統實現理論均達到以較少之實際風洞試驗結果來有效地識別系統參數的目的。我們利用現有輸入參數及不同識別變數延伸到多層遞迴式神經網路架構，估計最佳氣動力參數，此不但可以減少實際風洞吹試需求，更能節約測試資源及冗長時間。

This dissertation presents a new approach to deal with system identification for flight vehicle models using the eigensystem realization algorithm. The system identification is used to achieve the desired parameters of unmanned aerial vehicles (UAVs) in low-speed wind tunnel (LSWT) test and high-speed wind tunnel (HSWT) test.
The linear system identification applies a fuzzified eigensystem realization algorithm (fuzzified-ERA) for identification of the flight vehicle models in LSWT and HSWT. A variety of variables in model types and testing environment, such as tunnel wind speed, angle-of-attack, sideslip angle, elevator, mini-UAV model profile, and power system (motor and propeller) are considered in a power-on mini-UAV testing system in LSWT and an Advisory Group for Aerospace Research and Development (AGARD) standard calibration model in HSWT. The method based on the fuzzy logic inference structure is simple and effective. The results obtained are compared to those obtained by the conventional wind tunnel testing method. To verify effectiveness of the proposed methodology, simulations are conducted using the real-world experimental data that demonstrate the working performance of the proposed method correlates well as expected.
The relationship of variables for flight vehicles in wind tunnel test is highly nonlinear. To fulfill aerodynamic parameter identification of flight vehicle models, an eigensystem realization algorithm identification method of ERA based on a nonlinear multilayer recurrent neural network (MRNN) is also proposed. ERA is a mathematical method, which purpose is to use measurements observed over time, containing random variations and other inaccuracies from the input data, and produce values that tend to be closer to the true values. For the MRNN, it is included to estimate the optimal parameters of the nonlinear flight vehicle model. We apply the results of linear ERA membership function as the initial weights of the nonlinear ERA in RNN model parameter identification and determine the optimal weights to identify aerodynamic coefficients of flight vehicles with less testing. Simulation results preliminarily validate that the method resulted from linear and nonlinear system with eigensystem realization algorithms.
Considering the practical usefulness, the approaches presented in this dissertation efficiently help aerodynamicists selecting the optimal design parameters to meet the desired goal and reduce the cost for conducting real-world wind tunnel testing for flight vehicles.

中文摘要 I
Abstract II
Contents V
List of Tables VII
List of Figures VIII
Chapter 1 Introduction 1
1.1 Motivations 1
1.2 Literature Review 4
1.2.1 Review of Eigenvalue Realization Algorithms (ERAs) 4
1.2.2 Review of Nonlinear Identification Based on Multilayer Recurrent Neural Network (MRNN) 6
1.3 Contributions of the Dissertation 7
1.4 Organization of the Dissertation 8
Chapter 2 Wind Tunnel System Descriptions 9
2.1 Low Speed Wind Tunnel 9
2.2 High Speed Wind Tunnel 11
2.3 Wind Tunnel Forces and Moments 12
2.4 Wind Tunnel Data Corrections 19
2.4.1 Balance Corrections 19
2.4.2 Wall Corrections 21
2.5 Measurement Errors 21
Chapter 3 Linear ERA-Based Model Identification 22
3.1 ERA 22
3.2 Fuzzified Eigenvalue Realization Algorithm (Fuzzified -ERA) 24
3.2.1 Fuzzy Logic Design 24
3.2.2 Fuzzy Correlation Coefficient 36
3.2.3 Fuzzified-ERA 37
3.3 Results and Discussions 39
3.3.1 Verification of Different Wind Tunnel Speed and Rotational Speed in LSWT 40
3.3.2 Verification of Different Angle of Elevator in LSWT 45
3.3.3 Verification of Different Wind Tunnel Speed in Higher-Order in LSWT 50
3.3.4 Verification of Different Wind Tunnel Speed in HSWT 56
3.4 Concluding Remarks 60
Chapter 4. Nonlinear Identification Based on Multilayer Recurrent Neural Network 61
4.1 Nonlinear RNN System Description 61
4.2 Nonlinear Parameter Identification Description 62
4.2.1 Learning Algorithm of RNN 67
4.2.2 Systemic error threshold 69
4.2.3 Learning rate 71
4.3 Nonlinear MRNN in ERA-Based Identification Algorithm 71
4.3.1 Nonlinear MRNN Description 72
4.3.2 Learning Algorithm of MRNN 75
4.3.3 Main Results 77
4.4 Results and Discussions 78
4.4.1 Verification of linear and nonlinear systems with RNN in HSWT 81
4.4.2 Verification of single variable with RNN in HSWT 84
4.4.3 Verification of linear and nonlinear systems with MRNN in LSWT 87
4.4.4 Verification of multivariable in MRNN with LSWT 92
4.5 Concluding Remarks 97
Chapter 5. Conclusions and Future Works 98
5.1 Conclusions 98
5.2 Future Works 99
Bibliography 100
Publication List 108

[1]D. P. Raymer, Aircraft Design: A Conceptual Approach, AIAA Education Series, 1992.
[2]F. O. Smetana, Flight Vehicle Performance and Aerodynamic Control, AIAA Education Series, 2001.
[3]J. N. Juang and R. S. Pappa, “ An Eigensystem Realization Algorithm (ERA) for Model Parameter Identification and Model Reduction,” Journal of Guidance, Control, and Dynamics, vol. 8, no. 5, pp. 620-627, Sept. 1985.
[4]F. J. P. Cebolla, A. Martinez, B. Martin, E. Laloya, C. E. Montano, S. Mendez and J. E. Vicuna, “Experimental equivalent circuit parameters identification of a switched reluctance motor,” in Proc. IEEE Annual Conference Industrial Electronics, pp. 1140-1145, Nov. 2009.
[5]C. W. Chen, J. K. Huang, M. Phan and J. N. Juang, “Integrated System Identification and State Estimation for Control of Large Flexible Space Structures,” Journal of Guidance, Control, and Dynamics, vol. 15, no. 1, pp. 88-95, Jan. 1992.
[6]W. Lan, C. K. Thum and B. M. Chen, “A Hard-Disk-Drive Servo System Design Using Composite Nonlinear-Feedback Control With Optimal Nonlinear Gain Tuning Methods, ” IEEE Transactions on Industrial Electronics, vol. 57, no. 5, pp. 1735-1745, May 2010.
[7]A. S. Abdel-Khalik, M. I. Masoud, B. W. Williams, A. L. Mohamadein and M. M. Ahmed, “Steady-State Performance and Stability Analysis of Mixed Pole Machines With Electromechanical Torque and Rotor Electric Power to a Shaft-Mounted Electrical L,” IEEE Transactions on Industrial Electronics, vol. 57, no. 1, pp. 22-34, Jan. 2010.
[8]K. B. Smida, P. Bidan, T. Lebey, F. B. Ammar and M. Elleuch, “Identification and Time-Domain Simulation of the Association Inverter Cable Asynchronous Machine Using Diffusive Representation,” IEEE Transactions on Industrial Electronics, vol. 56, no. 1, pp. 257-265, Jan 2009.
[9]J. J. Sanchez-Gasca, “Computation of Turbine-Generator Subsynchronous Torosional Modes From Measured Data Using the Eigensystem Realization Algorithm,” in Proc. IEEE Power Engineering Society Winter Meeting, pp. 1272-1276, 2001.
[10]R.W. Longman, J. S. Lew, D. H. Tseng and J. N. Juang, “Variance and Bias Computation for Improved Modal Identification Using ERA/DC,” in Proc. American Control Conference, pp. 3013-3018, June 1991.
[11]R. H. Abiyev and O. Kaynak, “Type 2 Fuzzy Neural Structure for Identification and Control of Time-Varying Plants,” IEEE Transactions on Industrial Electronics, vol. 57, no. 12, pp.4147 - 4159, Dec. 2010.
[12]M. Ta, D. Zhou and V. DeBrunner, “Application of Minimum Entropy Estimation in Modal Analysis,” in Proc. Signal Processing Education Workshop, pp. 193-196, Sept. 2006.
[13]J. N. Juang, J. E. Cooper and J. R. Wright, “An Eigensystem Realization Algorithm Using Data Correction (ERA/DC) for Modal Parameter Identification,” Journal of Control Theory Advance Technology, vol. 4, no. 1, pp. 5-14, March 1988.
[14]L. D. Peterson, “Efficient Computation of the Eigensystem Realization Algorithm,” Journal of Guidance, Control, and Dynamics, vol. 18, no. 3, pp. 395-403, May 1995.
[15]C. H. Lee and C. C. Teng, “Identification and Control of Dynamic Systems Using Recurrent Fuzzy Neural Networks,” IEEE Transactions on Fuzzy System, vol. 8, no. 4, pp. 349-366, Aug. 2000.
[16]T. N. Shiau, C. H. Cheng and M. S. Tsai, “Application of Singular Value Decomposition Technique to System Identification by Doping an Optimum Signal,” Journal of Chinese Society Mechanic Engineering, vol. 28, no. 6, pp. 605-616, 2007.
[17]C. Xia, C. Guo and T. Shi, “A Neural Network Identifier and Fuzzy Controller Based Algorithm for Dynamic Decoupling Control of Permanent Magnet Spherical Motor,” IEEE Transactions on Industrial Electronics, vol. 57, no. 8, pp. 2868- 2878, 2010.
[18]G. Mogenier, R. Dufour, G. Ferraris-Besso, L. Durantay and N. Barras, “Identification of Lamination Stack Properties: Application to High-Speed Induction Mot,” IEEE Transactions on Industrial Electronics, vol. 57, no. 1, pp. 281-287, 2010.
[19]S. P. Won, F. Golnaraghi and W. W. Melek, “A Fastening Tool Tracking System Using an IMU and a Position Sensor With Kalman Filters and a Fuzzy Expert System,” IEEE Transactions on Industrial Electronics, vol. 56, no. 5, pp. 1782-1792, 2009.
[20]S. P. Won, W. W. Melek and F. Golnaraghi, “A Kalman/Particle Filter-Based Position and Orientation Estimation Method Using a Position Sensor/ Inertial Measurement Unit Hybrid System,” IEEE Transactions on Industrial Electronics, vol. 57, no. 5, pp. 1787-1798, May 2010.
[21]S. A. Brabdt, R. J. Stiles, J. J. Bertin and R. Whitford, Introduction to Aeronautics: A Design Perspective, AIAA Education Series, 1801.
[22]A. Carvalho, M. C. Gonzalez, P. Costa and A. Martins, “Issues on Performance of Wind Systems Derived from Exploitation Data,” in Proc. IEEE Annual Conference Industrial Electronics, pp. 3599-3605, Nov. 2009.
[23]W. H. Rae and A. Pope, Low-Speed Wind Tunnel Testing, John Wiley & Sons, 1984.
[24]C. H. Huang, C. S. Lee and C. L. Lin, “Application of Fuzzified-ERA in Parameter Identification of Mini-UAV LSWT Testing System,” in Proc. National Conference Fuzzy Theory and Its Applications, 2009. http://fuzzy2009.nuk.edu.tw/announce/ ACCEPT_LIST.pdf.
[25]C. H. Huang, C. L. Lin and M. J. Chao, “FERA in Parameter Identification with Application in Low Speed Wind Tunnel Test,” Corrected Proof by Aerospace Science and Technology, Engineering, Aerospace, Oct. 2010. http://dx.doi.org/10.1016/ j.ast.2010.10.002.
[26]A. Janczak, Identification of Nonlinear Systems Using Neural Networks and Polynomial Models, Springer-Verlag, New York, 2005.
[27]O. Nelles, Nonlinear System Identification, Springer-Verlag, New York, 2001.
[28]T.W.S. Chow and Y. Fang , “A Recurrent Neural Network Based Real-time Learning Control Strategy Applying to Nonlinear Systems with Unknown Dynamics ,” Journal of IEEE Transactions on Industrial Electronics, vol. 45, no. 1, pp. 151-161, 1998.
[29]C. J. Lin, “Wavelet Neural Networks with a Hybrid Learning Approach,” Journal of Information Science and Engineering, vol. 22, pp. 1367-1387, 2006.
[30]C. J. Lin and Y. J. Xu, “Design of Neuron-fuzzy Systems Using a Hybrid Evolutionary Learning Algorithm,” Journal of Information Science and Engineering, vol. 23, pp. 463-477, 2007.
[31]J.T. Kuan and M.K. Chen, “Parameter Evaluation for Lightning Impulse with Oscillation and Overshoot Using the Eigensystem Realization Algorithm,” IEEE Transactions on Dielectrics and Electrical Insulation, vol. 13, no. 6, pp. 1303-1016, Dec. 2006.
[32]C. H. Huang, C. L. Lin, C. S. Lee and M. J. Chao, “Identification of Flight Vehicle Models Using Fuzzified Eigensystem Realization Algorithm,” IEEE Transactions on Industrial Electronics, http://ieeexplore.ieee.org/stamp/stamp.jsp? arnumber=05715869, 2011.
[33]J. S. Wang and Y. L. Hsu, “Dynamic Nonlinear System Identification Using a Wiener-type Recurrent Network with OKID Algorithm,” Journal of Information Science and Engineering, vol. 24, pp.891-905, 2008.
[34]L. Yang and Y. Xue, Development of a New Recurrent Neural Network Toolbox (RNN-Tool), McMaster University, Hamilton, Ontario, 2006.
[35]B. L. Stevens and F. L. Lewis, Aircraft Control and Simulation, John Wiley & Sons, 1992.
[36]T. Kinoshita and F. Imado, “A Study on the Optimal Flight Control for an Autonomous UAV,” in Proc. International IEEE Conference Mechatronics and automation, pp. 996-1001, June 2006.
[37]A. Pope and J. J. Harper, Low-Speed Wind Tunnel Testing, John Wiley & Sons, 1992.
[38]C. T. Lin and C. S. George Lee, Neural Fuzzy Systems, Prentice-Hall, 1996.
[39]D. Wang, X. J. Zeng and J. A. Keane, “An Input-Output Clustering Method for Fuzzy System Identification,” in Proc. IEEE Conference Fuzzy Systems, pp. 224-230, July 2007.
[40]M. Morelli, “Analysis of Some Interference Effects in a Transonic Wind Tunnel,” Journal of Aircraft, vol. 32, no. 3, pp. 501-509, May-June 1995.
[41]K. S. Tang, K. F. Man, Z. F. Liu and S. Kwong, “Minimal Fuzzy Memberships and Rules Using Hierarchical Genetic Algorithms,” IEEE Transactions on Industrial Electronics, vol. 45, no. 1, pp. 162-169, Feb 1998.
[42]C. F. Juang, C. M. Lu, C. Lo and C. Y. Wang, “Ant Colony Optimization Algorithm for Fuzzy Controller Design and Its FPGA Implementation” IEEE Transactions on Industrial Electronics, vol. 55, no. 3, pp. 1453-1462, 2008.
[43]M. Shimakawa, “Calculus of Interpolated Fuzzy Relation Type Fuzzy Reasoning Method,” in Proc. International Conference Innovative Computing, Information and Control, pp. 839-851, Aug. 2007.
[44]N. Thouault, C. Breitsamter, C. Gologan and N. A. Adams, “Numerical Analysis of Design Parameters for a Generic Fan-in-Wing Configuration,” Aerospace Science and Technology, vol. 14, no. 2, pp. 65-77, 2010.
[45]D. Rivas, L. G. Oscar, S. Esteban and E. Gallo, “An Analysis of Maximum Range Cruise Including Wind Effects,” Aerospace Science and Technology, vol. 14, no. 2, pp. 38-48, 2010.
[46]N. P. Lin and H. E. Chueh, “Text Multi-Categorization Based on Fuzzy Correlation Analysis,” WSEAS Transactions on Systems, vol. 2, no. 6, pp. 273-278, 2007.
[47]J. T. Kuan and M. K. Chen, “Parameter Evaluation for Lightning Impulse with Oscillation and Overshoot Using the Eigensystem Realization Algorithm,” IEEE Transactions on Dielectrics and Electrical Insulation, vol. 13, no. 6, pp. 1303-1016, Dec. 2006.
[48]C. F. Hung, W. J. Ko and Y. T. Peng, “Application of ARX/ERA to Identify the Equivalent State Equation of Motion,” in Proc. Bulletin of the College of Engineering, NTU, vol.1, no. 82, pp. 21-31, June 2001.
[49]K. Saiki, A. Hara, K. Sakata and H. Fujimoto, “A Study on High-Speed and High-Precision Tracking Control of Large-Scale Stage Using Perfect Tracking Control Method Based on Multirate Feedforward Control,” IEEE Transactions on Industrial Electronics, vol. 57, no. 4, pp. 1393-1400, April 2010.
[50]J. N. Juang, Applied System Identification, Prentice-Hall, Englewood Cliffs, NJ, 1994.
[51]J. N. Juang and M. Q. Phan, Identification and Control of Mechanical System, Cambridge University Press, New York, 2001.
[52]F. U. Syed, M. L. Kuang, M. Smith, S. Okubo and H. Ying, “Fuzzy Gain-Scheduling Proportional-Integral Control for Improving Engine Power and Speed Behavior in a Hybrid Electric Vehicle,” IEEE Transactions on Vehicular Technology, vol. 58, no. 1, pp. 69-84, Jan. 2009.
[53]K. S. Narendra, “Neural Networks for Control: Theory and Practice,” Proceedings of International Conference on IEEE, vol. 84, no. 10, pp. 1385-1406, 1996.
[54]A.Y. Alanis, E.N. Sanchez, A.G. Loukianov and M.A. Perez, “Real-Time Recurrent Neural State Estimation,” Journal of IEEE Transactions on Neural Networks, vol. 22, no. 3, pp.497-505, March 2011.
[55]G. Gattu and E. Zafiriou, “Nonlinear Quadratic Dynamic Matrix Control with State Estimation,” Industrial & Engineering Chemistry Research, vol. 31, pp. 1096-1104, 1992.
[56]M. Marwaha, J. Valasek and P. Singla, “GLOMAP Approach for Nonlinear System Identification of Aircraft Dynamics Using Flight Data,” Journal of AIAA-2008-6895, AIAA Atmospheric Flight Mechanics Conference and Exhibit, Honolulu, Hawaii, August 2008.
[57]L. Chen and K. S. Narendra, “Identification and Control of a Nonlinear Discrete-time System Based on Its Linearization: A Unified Framework,” Journal of IEEE Transactions on Neural Networks, vol. 15, no. 3, pp.663-673, May 2004.
[58]W. Yu, “Nonlinear System Identification Using Discrete-Time Recurrent Neural Networks with Stable Learning Algorithms,” Information Sciences, vol. 158, no. 1, pp.131-147, 2004.
[59]M. Li, Y. Shu and C.Yang, “On-line Learning Algorithm of Nonlinear Adaptive Control Systems Based on Signal Flow Graph Theory,” Proceedings of the 27th Chinese Control Conference Kunming, Yunnan, China, pp.11-15, July 2008.
[60]X. Wang and Y. Huang, “Convergence Study in Extended Kalman Filter-Based Training of Recurrent Neural Networks,” Journal of IEEE Transactions on Neural Networks, vol. 22, no. 4, pp.588-600, 2011.
[61]I. Skrjanc and D. Matko, “Fuzzy Predictive Functional Control in the State Space Domain,” Journal of Intelligent and Robotic Systems, vol. 31, pp. 283-297, 2001.
[62]A. M. Shaw and F. J. Doyle, “Multivariable Nonlinear Control Applications for a High Purity Distillation Column Using a Recurrent Dynamic Neuron Model,” Journal of Process Control, vol. 7, no. 4, pp. 255-268, August 1997.
[63]D.G. Kelly, “Stability in Contractive Nonlinear Neural Networks,” IEEE Transactions on Biomedical Engineering, vol. 37, no. 3, pp. 231-242, 1990.
[64]X. B. Liang and T. Yamaguchi, “On the Analysis of Global and Absolute Stability of Nonlinear Continuous Neural Networks,” Transactions on Fundamentals of Electronics, Communications and Computer Sciences, vol. E80-A, no. 1, pp. 223-229, 1997.
[65]A. M. Schaefer and S. Udluft, “Solving Partially Observable Reinforcement Learning Problems with Recurrent Neural Networks,” Reinforcement Learning in Non-Stationary Environments, Workshop Proceedings of the European Conference on Machine Learning (ECML-05), 2005.
[66]T. G. Barbounis and J. B. Theocharis, “Locally Recurrent Neural Networks Optimal Filtering Algorithms: Application to Wind Speed Prediction Using Spatial Correlation,” Proceedings of International Joint Conference on Neural Networks, Montreal, Canada, July 31-August 4, 2005.
[67]W. Yu and X. Li, “Fuzzy Identification Using Fuzzy Neural Networks With Stable Learning Algorithms,” IEEE Transactions on Fuzzy Systems, vol. 12, no. 3, pp. 411-420, 2004.
[68]I. Skrjanc, S. Blazic and O. Agamennoni, “Interval Fuzzy Modeling Applied to Wiener Models with Uncertainties,” ISA Transactions, vol. 43, pp. 585-595, 2004.
[69]W. Yu and Xiaoou Li, “Automated Nonlinear System Modeling with Multiple Fuzzy Neural Networks and Kernel Smoothing,” International Journal of Neural Systems, vol. 20, no.5, pp.429-435, 2010.
[70]D.E. Rumelhart, G.E. Hinton and R.J. Williams, “Learning Representations by Back-Propagating Errors,” International Weekly Journal of Science on Nature, vol. 323, pp.533-536, October 1986.