博碩士論文 965401016 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:50 、訪客IP:18.188.76.209
姓名 董聖龍(Shen-Lung Tung)  查詢紙本館藏   畢業系所 電機工程學系
論文名稱 粒子群演算法於二階時變系統穩定分析與穩定化設計
(Stability and Stabilization for Second-Order Time-Varying Systems Based on PSO Algorithm)
相關論文
★ 小型化 GSM/GPRS 行動通訊模組之研究★ 語者辨識之研究
★ 應用投影法作受擾動奇異系統之強健性分析★ 利用支撐向量機模型改善對立假設特徵函數之語者確認研究
★ 結合高斯混合超級向量與微分核函數之 語者確認研究★ 敏捷移動粒子群最佳化方法
★ 改良式粒子群方法之無失真影像預測編碼應用★ 粒子群演算法應用於語者模型訓練與調適之研究
★ 粒子群演算法之語者確認系統★ 改良式梅爾倒頻譜係數混合多種語音特徵之研究
★ 利用語者特定背景模型之語者確認系統★ 智慧型遠端監控系統
★ 正向系統輸出回授之穩定度分析與控制器設計★ 混合式區間搜索粒子群演算法
★ 基於深度神經網路的手勢辨識研究★ 人體姿勢矯正項鍊配載影像辨識自動校準及手機接收警告系統
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   [檢視]  [下載]
  1. 本電子論文使用權限為同意立即開放。
  2. 已達開放權限電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。
  3. 請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。

摘要(中) 本論文係研究二階向量微分線性時變系統之指數穩定性分析及穩定化控制設計,研究的範疇包含奇異與非奇異等兩大系統,而且系統中含有的非線性擾動因子,也在本文的探討之列。本研究之技術關鍵,乃藉由估算線性時變系統狀態軌跡大小的邊界,將時變系統的穩定問題,轉換為非時變系統的穩定問題。進而,推導出時變系統的指數型穩定性分析及穩定化設計準則。而且為解出最佳的指數型穩定分析準則與穩定化控制器,本論文也提出一種命名為「適應性模糊粒子群最佳化演算法」(AFPSO)。此AFPSO乃是利用模糊理論,適應性地動態調整粒子群最佳化演算法(PSO)之兩個加速度參數,以改善粒子搜尋的精準度與效率性。而AFPSO又可彈性的衍生出多種的變種演算法則,進一步地改善粒子全域搜尋的能力與效率。例如,結合二次插補交叉的計算,形成AFPSO-QI演算法;或併入限制因子,形成AFPSO-cf演算法。最後,本論文提出的穩定化設計法則,應用至三個自由度(3DOF)的雙軸拖曳車之前軸避震系統上。經由模擬的結果顯示,本法可使得車輛的震動及擺動狀態,快速達到指數式的衰減穩定,改善駕駛的舒適感與車輛的操控性。
摘要(英) Stability and Stabilization for Second-Order Time-Varying Systems Based on PSO Algorithm
Abstract
In this dissertation, uniformly exponential stability analysis and stabilization design of linear time-varying systems represented by the second-order vector differential equations are concerned. The systems with a singular or a nonsingular leading coefficient matrix and with bounded nonlinear uncertainties are all discussed. Using bounding techniques on the trajectories of linear time-varying systems, the stability problem of linear time-varying systems is transformed to that of linear time-invariant systems. Then the sufficient conditions for uniformly exponential stability and stabilization are derived. Besides, an adaptive fuzzy particle swarm optimization (AFPSO) algorithm is also proposed for solving the optimization problems of uniformly exponential stability and stabilization. The proposed AFPSO utilizes fuzzy set theory to adjust the PSO acceleration coefficients adaptively to improve the search accuracy and efficiency. Two variants of AFPSO are constructed by incorporating with the quadratic interpolation and the crossover operator called the AFPSO-QI as well as integrating with a constriction factor called the AFPSO-cf, these may further improve global searching ability and effectiveness. Finally, the proposed method is applied to active suspension systems for a three-degree-of-freedom (3-DOF) twin-shaft vehicle of front axle suspension. The simulation results show that all the states of vehicle can be guaranteed in an optimal exponential decay in nearly real-time.
關鍵字(中) ★ 時變系統
★ 二階向量
★ 粒子群最佳化演算法
關鍵字(英) ★ time-varying system
★ second-order vector
★ PSO
論文目次 List of Figures III
List of Tables V
Chapter 1 Introduction 1
1.1 Motivation and review of related research 1
1.2 Organization of this dissertation 5
Chapter 2 Preliminaries for second-order systems and AFPSO 7
2.1 Notations 7
2.2 AFPSO for multimodal functions 10
2.2.1 Standard PSO algorithm 10
2.2.2 Proposed AFPSO algorithm 13
2.2.3 A variant of AFPSO: AFPSO-QI 19
2.2.4 Simulation results 19
2.2.5 Results for 10-Dimension minimization problems 24
2.2.6 Results for 30-Dimension minimization problems 29
2.3 Summary 33
Chapter 3 Exponential stability of second-order systems 35
3.1 Singular case 35
3.2 Nonsingular case 42
3.3 Simulation results 45
3.4 Summary 49
Chapter 4 Exponential stability of second-order systems with bounded nonlinearities 50
4.1 Problem formulation 50
4.2 Singular case 50
4.3 Nonsingular case 56
4.4 Simulation results 60
4.5 Summary 63
Chapter 5 AFPSO for exponential stabilization of second-order systems 64
5.1 Stabilization for second-order systems 64
5.2 A variant of AFPSO: AFPSO-cf 66
5.3 Simulation results 67
5.4 Summary 72
Chapter 6 Exponential stabilization in active suspension systems 73
6.1 Overview of suspension systems 73
6.2 Plant model of a tractor with the front axle suspension 75
6.3 Stabilization for second-order systems with bounded nonlinearities 77
6.4 Controller design 79
6.5 Frequency domain analysis 81
6.6 Time domain analysis 84
6.7 Summary 89
Chapter 7 Conclusions 90
References 93
Publication List 102
參考文獻 [1] Y. Fujisaki, M. Ikeda, and K. Miki, “Robust stabilization of large space structures via displacement feedback,” IEEE Transactions on Automatic Control, vol. 46, no. 12, pp. 1993-1996, 2001.
[2] K. Inoue, S. Yamamoto, T. Ushio, and T. Hikihara, “Torque-based control of whirling motion in a rotating electric machine under mechanical resonce,” IEEE Transactions on Control Systems Technology, vol. 11, no. 3, pp. 335-344, 2003.
[3] S. L. Campbell and N. J. Rose, “A second order singular linear system arising in electric power systems analysis,” International Journal of Systems Science, vol. 13, no. 1, pp. 101-108, 1982.
[4] H. Tasso, “On Lyapunov stability of dissipative mechanical systems,” Physics Letters A, vol. 257, no. 5-6, pp. 309-311, 1999.
[5] H. Tasso and G. N. Throumoulopoulos, “On Lyapunov stability of nonautonomous mechanical systems,” Physics Letters A, vol. 271, no. 5-6, pp. 413-418, 2000.
[6] J. Cao, H. Liu, P. Li, and D. J. Brown, “State of the art in vehicle active suspension adaptive control systems based on intelligent methodologies,” IEEE Transactions on Intelligent Transportation System, vol. 9, no. 3, pp. 392-405, 2008.
[7] H. Nizar, T. Lahdhiri, D. S. Joo, J. Weaver, and A. Faysal, “Sliding mode neural network inference fuzzy logic control for active suspension systems,” IEEE Transactions on Fuzzy System, vol. 10, no. 2, pp. 234-246, 2002.
[8] N. Yagiz, Y. Hacioglu, and Y. Taskin, “Fuzzy sliding-mode control of active suspensions,” IEEE Transactions on Industrial Electronics, vol. 55, no. 11, pp. 3883-3890, 2008.
[9] K. Liu and F. L. Lewis, “Continuous robust controller guaranteeing ESL for second-order dynamic systems,” International Journal of System Science, vol. 24, no. 11, pp. 2019-2031, 1993.
[10] K. Nonami and T. Ito, “ synthesis of flexible rotor-magnetic bearing systems,” IEEE Transactions on Control Systems Technology, vol. 4, no. 5, pp. 503-512, 1996.
[11] J. M. Martin and G. A. Hewer, “Smallest destabilizing perturbations for the linear systems,” International Journal of Control, vol. 45, no. 5, pp. 1495-1504, 1987.
[12] J. Fei, “Robust adaptive vibration tracking control for a micro-electro-mechanical systems vibratory gyroscope with bound estimation,” IET Control Theory & Applications, vol. 4, no. 6, pp. 1019-1026, 2010.
[13] E. E. Zajac, “The Kelvin-Tait-Chetaev theorem and extensions,” Journal of Astronautical Sciences, vol. 11, no. 2, pp. 46-49, 1964.
[14] A. Zevin and M. Pinsky, “Exponential stability and solution bounds for systems with bounded nonlinearities,” IEEE Transactions on Automatic Control, vol. 48, no. 10, pp. 1779-1804, 2003.
[15] J. Sun, Q. G. Wang, and Q. C. Zhong, “A less conservative stability test for second-order linear time-varying vector differential equations,” International Journal of Control, vol. 80, no. 4, pp. 523-526, 2007.
[16] M. I. Gil’, “Stability of linear systems governed by second order vector differential equations,” International Journal of Control, vol. 78, no. 7, pp. 534-536, 2005.
[17] K. Inoue and T. Kato, “A stability condition for a time-varying system represented by a couple of a second- and a first-order differential equations,” Proceedings of the 43rd IEEE Conference on Decision and Control, 14-17 December 2004 , Atlantis, Paradise Island, Bahamas, pp. 2934-2935.
[18] K. Gu, V. L. Kharitonov, and J. Chen, Stability of Time-Delay Systems, New York: Birkhauser, 2003.
[19] J. Kennedy and R. Eberhart, “Particle swarm optimization,” Proceedings of the 4th IEEE International Conference on Neural Networks, November/ December 1995, Perth, WA, Australia, pp. 1942-1948.
[20] J. Kennedy, R. Eberhart, and Y. Shi, Swarm Intelligence, California: Morgan Kauffman, 2001.
[21] Y. Shi and R. Eberhart, “A modified particle swarm optimizer,” Proceedings of IEEE congress on Evolutionary Computation, 4-9 May 1998, Anchorage, AK , USA, pp. 69–73.
[22] K. E. Parsopoulos and M. N. Vrahatis, “UPSO: a unified particle swarm scheme,” Proceedings of the International Conference of Computational Methods in Sciences and Engineering, 2004, Lecture Series on Computer and Computational Sciences, Vol. 1, pp. 868-873.
[23] P. J. Angeline, “Using selection to improve particle swarm optimization,” Proceedings of IEEE congress on Evolutionary Computation, 4-9 May 1998, Anchorage, AK , USA, pp. 84-89.
[24] V. Miranda, “Evolutionary algorithm with particle swarm movements,” Proceedings of the 13th International Conference on Intelligent Systems Application to Power Systems, 6-10 November 2005, Arlington, VA , pp. 6-21.
[25] V. Miranda and N. Fonseca, “EPSO-best-of-two-worlds meta-heuristic applied to power system problems,” Proceedings of IEEE congress on Evolutionary Computation, 12-17 May 2002, Honolulu, HI, pp. 12-17.
[26] V. Miranda and N. Fonseca, “EPSO-evolutionary particle swarm optimization, a new algorithm with applications in power systems,” Proceedings of IEEE Transmission Distribution Conference and Exhibition, 6-10 October 2002, vol. 2, pp. 745-750.
[27] M. Pant, T. Radha, and V. P. Singh, “A new particle swarm optimization with quadratic interpolation,” Proceedings of IEEE International Conference on Computational Intelligence and Multimedia Applications, 13-15 December 2007, Sivakasi, Tamil Nadu, pp. 55-60.
[28] J. J. Liang and P. N. Suganthan, “Dynamic multi-swarm particle swarm optimizer,” Proceedings of IEEE on Swarm Intelligence Symposium, 8-10 June 2005, pp.124-129.
[29] W. Du and B. Li, “Multi-strategy ensemble particle swarm optimization for dynamic optimization,” Information Sciences, vol. 178, no. 15, pp. 3096-3109, 2008.
[30] R. Mendes, J. Kennedy, and J. Neves, “The fully informed particle swarm: simpler, maybe better,” IEEE Transactions on Evolutionary Computation, vol. 8, no. 3, pp. 204-210, 2004.
[31] J. J. Liang, A. K. Qin, P. N. Suganthan, and S. Baskar, “Comprehensive learning particle swarm optimizer for global optimization of multimodal functions,” IEEE Transactions on Evolutionary Computation, vol. 10, no. 3, pp. 281-296, 2006.
[32] G. Zhang, J. Jiang, Z. Su, M. Qi, and H. Fang, “Searching for overlapping coalitions in multiple virtual organizations,” Information Sciences, vol. 180, no. 17, pp. 3140-3156, 2010.
[33] S. T. Hsieh, T. Y. Sun, C. C. Liu, and S. J. Tsai, “Efficient population utilization strategy for particle swarm optimizer,” IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 39, no. 2, pp. 444-456, 2009.
[34] M. Clerc and J. Kennedy, “The particle swarm-explosion, stability, and convergence in a multidimensional complex space,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 1, pp. 58-73, 2002.
[35] Y. Fang and T. G. Kincaid, “Stability analysis of dynamical neural networks,” IEEE Transactions on neural networks, vol. 7, no. 4, pp. 996-1006, 1996.
[36] P. Lancaster, Theory of Matrices, New York: Academic Press, 1985.
[37] M. Vidyasagar, Nonlinear Systems Analysis, New Jersey: Prentice-Hall, Inc., 2000.
[38] H. Jeffreys and B. Jeffreys, Methods of Mathematical Physics (3rd edition), Cambridge England: Cambridge University Press, pp. 53, 1988.
[39] I. Kiguradze and Z. Sokhadze, “On singular functional differential inequalities,” Georgian Mathematical Journal, vol. 4, no. 3, pp. 259-278, 1997.
[40] M. Dorigo, V. Maniezzo, and A. Colorni, “Ant system: optimization by a colony of cooperating agents,” IEEE Transactions on Systems, Man, and Cybernetics-Part B, vol. 26, no. 1, pp. 29-41, 1996.
[41] A. Colorni, M. Dorigo, and V. Maniezzo, “Distributed optimization by ant colonies,” Proceedings of ECAL'91-European Conference on Artificial Life, 1991, Paris, France, pp. 134-142.
[42] M. Dorigo, V. Maniezzo, and A. Colorni, “The ant system: an autocatalytic optimizing process,” Technical Report TR91-016, Dipartimento di Elettronica,Politecnico di Milano, Italy, 1991.
[43] C. W. Reynolds, “Flocks, herds and schools: a distributed behavioral modal,” Computer Graphics, vol. 21, no. 4, pp. 25-34, 1987.
[44] F. Heppner and U. Grenander, “A stochastic nonlinear model for coordinated bird flocks”, The Ubiquity of Chaos, AAAS Publications , pp. 233-238, 1990.
[45] M. O’Neil and A. Brabazon, “Self-organizing swarm (SOSwarm): a particle swarm algorithm for unsupervised learning,” Proceedings of IEEE congress on Evolutionary Computation, 16-21 July 2006, Vancouver, BC, Canada, pp. 634-639.
[46] K. E. Parsopoulos and M.N. Vrahatis, “On the computation of all global minimizers through particle swarm optimization,” IEEE Transactions on Evolutionary Computation, vol. 8, no. 3, pp. 211-224, 2004.
[47] Y. Wang and Y. Yang, “Particle swarm optimization with preference order ranking for multi-objective optimization,” Information Sciences, vol. 179, no. 12, pp. 1944-1959, 2009.
[48] N. Iwasaki, K. Yasuda, and G. Ueno, “Dynamic parameter tuning of particle swarm optimization,” IEEJ Transactions on Electrical and Electronic Engineering, vol. 1, no. 4, pp. 353-363, 2006.
[49] M. A. Montes de Oca, J. Pena, T. Stutzle, C. Pinciroli, and M. Dorigo, “Heterogeneous particle swarm optimizers,” Proceedings of IEEE congress on Evolutionary Computation, 18-21 May 2009, Trondheim , pp. 698-705.
[50] X. Yao, Y. Liu, and G. Lin, “Evolutionary programming made faster,” IEEE Transactions on Evolutionary Computation, vol. 3, no. 2, pp. 82-102, 1999.
[51] J. J. Liang, P. N. Suganthan, and K. Deb, “Novel composition test functions for numerical global optimization,” Proceedings of IEEE on Swarm Intelligence Symposium, 8-10 June 2005, pp. 68-75.
[52] P. N. Suganthan, N. Hansen, J. J. Liang, K. Deb, Y.-P. Chen, A. Auger, and S. Tiwari, “Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization,” Technical report, Nanyang Technological University, Singapore, 2005.
[53] R. Salomon, “Reevaluating genetic algorithm performance under coordinate rotation of benchmark functions,” BioSystems, vol. 39, no. 3, pp. 263-278, 1996.
[54] F. Wilcoxon, “Individual comparisons by ranking methods,” Biometrics Bulletin, vol. 1, no. 6, pp. 80-83, 1945.
[55] K. Inoue, S. Yamamoto, T. Ushio, and T. Hikihara, “Elimination of jump phenomena in a flexible rotor system via torque control,” Proceedings of the second Internal Conference on Control of Oscillations and Chaos, 2000, St. Petersburg , Russia , vol. 1, pp. 58-61.
[56] S. Z. Abdul-Rahman and G. Ahmadi, “Stability analysis of non-autonomous linear systems by a matrix decomposition method,” International Journal of System Science, vol. 17, no. 11, pp. 1645-1660, 1986.
[57] R. K. Yedavalli and S. R. Kolla, “Stability analysis of linear time-varying systems,” International Journal of System Science, vol. 19, no. 9, pp. 1853-1858, 1988.
[58] S. Wolfram, The Mathematica Book (5th edition), Champaign: Wolfram Media, Inc., 2003.
[59] S.-L. Tung, Y.-T. Juang, and W.-Y. Huang, “Stability analysis for linear systems with singular second-order vector differential equations,” IEEE Transactions on Automatic Control, vol. 56, no. 2, pp. 410-413, 2011.
[60] S.-L. Tung, Y.-T. Juang , W.-Y. Wu, and W.-Y. Shieh, “An improved stability test and stabilization of linear time-varying systems governed by second-order vector differential equations,” International Journal of Systems Science, in press, doi:10.1080/00207721003658210.
[61] Y.-T. Juang, S.-L. Tung, and H.-C. Chiu, “Adaptive fuzzy particle swarm optimization for global optimization of multimodal functions,” Information Sciences, in press, doi: 10.1016/j.ins.2010.11.025.
[62] L. E. Sakman, R. Guclu, and N. Yagiz, “Fuzzy logic control of vehicle suspensions with dry friction nonlinearity,” Sadhana, vol. 30, no. 5, pp. 649-659, 2005.
[63] D. Hrovat, “Survey of advanced suspension developments and related optimal control applications,” Automatica, vol. 33, no. 10, pp.1781-1817, 1997.
[64] I. Fialho and G. J. Balas, “Road adaptive active suspension design using linear parameter-varying gain-scheduling,” IEEE Transactions on Control Systems Technology, vol. 10, no. 1, pp. 43-54, 2002.
[65] W. Gao, N. Zhang, and H. P. Du, “A half-car model for dynamic analysis of vehicles with random parameters,” The 5th Australasian Congress on Applied Mechanics, 10-12 December 2007, Brisbane, Australia, pp. 595-600.
[66] C. MacLeod and R. M. Goodall, “Frequency-shaping LQ control of Maglev suspension systems for optimal performance with deterministic and stochastic inputs,” IEE Proceedings Control Theory & Applications, vol. 143, no. 1, pp. 25-30, 1996.
[67] R. Rajamani and J. K. Hedrick, “Adaptive observers for active automotive suspensions: theory and experiment,” IEEE Transactions on Control Systems Technology, vol. 3, no. 1, pp. 86-93, 1995.
[68] M. Sunwoo, K. C.Cheok, and N. J. Huang, “Model reference adaptive control for vehicle active suspension systems,” IEEE Transactions on Industrial Electronics, vol. 38, no. 3, pp. 217-222, 1991.
[69] M.A. Abido, “Optimal design of power system stabilizers using particle swarm optimization,” IEEE Transactions on Energy Conversion, vol. 17, no. 3, pp. 406-413, 2002.
[70] C.O. Ourique, E. C. Biscaia, and J. C. Pinto, “The use of particle swarm optimization for dynamic analysis in chemical processes,” Computers & Chemical Engineering, vol. 26, no. 12, pp. 1783-1793, 2002.
[71] E. A. Barbashin, Introduction to the Theory of Stability. Netherlands: Wolters-Noordhoff, Groningen, 1970.
[72] B. Lu, S. Zhu, and Y.Zhang, “Research on parameter matching of front axle suspension vehicle based on MATLAB,” Proceedings of the IEEE International Conference on Automation and Logistics, 18-21 August 2007, Jina, China , pp. 2441-2445.
[73] P. Y. Sun and H. Chen, “Multi-objective output-feedback suspension control on a half-car model,” Proceedings of IEEE Conference on Control Applications, 23-25 June 2003,vol. 1, pp. 290-295.
[74] H. Taghirad and E. Esmailzadeh, “Automobile passenger comfort assured through LQG/LQR active suspension,” Journal of Vibration and Control, vol. 4, no. 5, pp. 603-618, 1998.
[75] F. Yu, J.-W. Zhang, and D. A. Crolla, “A study of a Kalman filter active vehicle suspension system using correlation of front and rear wheel road inputs,” Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, vol. 214, no. 5, pp. 493-502, 2000.
[76] International Organization for Standardization, “Mechanical vibration and shock-evaluation of human exposure to whole-body vibration, Part 1: General requirements,” ISO 2631-1(2nd edition), 1997.
指導教授 莊堯棠(Yau-Tarng Juang) 審核日期 2011-5-27
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明