跳到主要內容

臺灣博碩士論文加值系統

(44.211.31.134) 您好!臺灣時間:2024/07/24 17:40
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:吳思賢
研究生(外文):Wu, Sih-Sian
論文名稱:對於在布朗及卜瓦松雜訊下經由無線網路的多目標H_2/H_∞自動車導引控制
論文名稱(外文):Multiobjective H_2/H_∞ Autonomous Ground Vehicle Guidance Control via Wireless Network under Wiener and Poisson Noises
指導教授:陳博現
指導教授(外文):Chen, Bor-Sen
口試委員:李柏坤黃志良徐勝均
口試日期:2018-07-09
學位類別:碩士
校院名稱:國立清華大學
系所名稱:電機工程學系所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2018
畢業學年度:106
語文別:英文
論文頁數:31
中文關鍵詞:取樣資料控制網路控制系統多目標H_2/H_∞控制非線性隨機系統自動車路徑追蹤
外文關鍵詞:Sampled-data controlNetwork control system (NCS)Multiobjective H_2/H_∞ control problemNonlinear stochastic jump diffusion systemAutonomous ground vehiclePath following
相關次數:
  • 被引用被引用:0
  • 點閱點閱:200
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
本文介紹了經由無線網路的多目標H_2 /H_∞自動車導引控制,可以同時最佳化H_2和H_∞。在現實中,由於道路狀況的變化,GPS的定位偏差或者路徑被阻擋,自動車導引系統總是遭受到連續和不連續的擾動。本研究提出了對於非線性自動車系統的多目標 H_2 /H_∞基於觀測器的導引控制設計,該系統受到網路引起的延遲,封包遺失,連續布朗雜訊和不連續卜瓦松雜訊的影響。其中,提出了一種等效的間接方法來解決複雜的非線性隨機多目標 H_2 /H_∞基於觀測器的導引控制設計問題。為了避免求解多目標 H_2 /H_∞控制問題的Hamilton-Jacobin不等式,採用Takagi-Sugeno模糊模型逼近非線性隨機自動車導引系統。因此,多目標 H_2 /H_∞基於觀測器的導引控制設計問題可以轉換為線性矩陣不等式的多目標問題。由於線性矩陣不等式多目標問題不易於有效求解Pareto最優解,因此應用了多目標進化演算法,用於解決多目標H_2 /H_∞基於觀測器的自動車導引控制問題。最後,給出了智能城市街道自動車輛導引控制的仿真實例,驗證了多目標 H_2 /H_∞基於觀測器的自動車導引控制設計的設計性能。
This study introduces a multiobjective optimal (MO) H_2/H_∞ observer-based guidance control design to achieve the optimal H_2 quadratic guidance and optimal H_∞ robust guidance simultaneous in the nonlinear stochastic autonomous ground vehicle guidance control via wireless network. First, an autonomous ground vehicle guidance system always suffers from continuous and discontinuous fluctuations in the practical perspective due to the change of the road condition, the positioning deviation of the GPS, or something to suddenly keep off. This study proposes a MO H_2/H_∞ observer-based guidance control for the nonlinear autonomous ground vehicle guidance system suffered from network-induced delays, packet dropouts, continuous Wiener noise, and discontinuous Poisson jump noise. An equivalent indirect method is proposed to solve the complex nonlinear stochastic MO H_2/H_∞ observer-based guidance control design problem. In order to avoid solving an Hamilton-Jacobin inequalities (HJIs)-constrained multiobjective optimal problem (MOP) for MO H_2/H_∞ control problem, the Takagi-Sugeno (T-S) fuzzy model is employed to approximate the nonlinear stochastic autonomous ground vehicle guidance system. So that, the HJIs-constrained MOP for MO H_2/H_∞ observer-based guidance control problem can be transformed to a linear matrix inequalities (LMIs)-constrained MOP. Since the LMIs-constrained MOP is not easy to efficiently solve for Pareto optimal solutions, an LMIs-constrained multiobjective evolutionary algorithm (MOEA) is also proposed to solve the MO H_2/H_∞ observer-based guidance control problem of autonomous ground vehicle via wireless network. Finally, a simulation example of autonomous ground vehicle guidance control at the streets in the smart city is given to illustrate the design procedure and performance of the proposed MO H_2/H_∞ autonomous ground vehicle observer-based guidance control design.
摘要(i)
Abstract(ii)
致謝(iii)
Contents (iv)
I. Introduction(1)
II. System Description and Preliminaries(3)
III. MO Observer-based Guidance Control Design for Autonomous Ground Vehicle through Wireless Network(8)
IV. MO Fuzzy Observer-based Guidance Control Design for Autonomous Ground Vehicle through Wireless Network(10)
V. MO Observer-based Guidance Control Design for Autonomous Ground Vehicle through Wireless Network via two-step LMIs-constrained MOEA(16)
VI. Simulation Example and Result(18)
VII. Conclusion(23)
Appendix A (24)
Appendix B (24)
Appendix C (26)
Appendix D (29)
References (30)
[1]Y. Chen, L. Li, Advances in Intelligent Vehicles, New York, NY, USA:Academic, 2014.
[2]M. A. Zakaria, H. Zamzuri, R. Mamat, and S. A. Mazlan, ”A Path Tracking Algorithm Using Future Prediction Control with Spike Detection for an Autonomous Vehicle Robot,” International Journal of Advanced Robotic Systems, vol. 10, p. 309, Aug. 2013.
[3] S. A. Arogeti and N. Berman, ”Path Following of Autonomous Vehicles in the Presence of Sliding Effects,” IEEE Transactions on Vehicular Technology, vol. 61, no. 4, pp. 1481-1492, May 2012.
[4] R. Wang, H. Jing, C. Hu, F. Yan and N. Chen, ”Robust H1 Path Following Control for Autonomous Ground Vehicles With Delay and Data Dropout,” IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 7, pp. 2042-2050, July 2016.
[5] H. Fang, L. Dou, J. Chen, R. Lenain, B. Thuilot, and P. Martinet, ”Robust anti-sliding control of autonomous vehicles in presence of lateral disturbances,” Control Engineering Practice, vol. 19, pp. 468-478, 2011
[6] M. Choi and S. B. Choi, ”Model Predictive Control for Vehicle Yaw Stability With Practical Concerns,” IEEE Transactions on Vehicular Technology, vol. 63, no. 8, pp. 3539-3548, Oct. 2014.
[7] W. Sun, H. Gao and B. Yao, ”Adaptive Robust Vibration Control of Full-Car Active Suspensions With Electrohydraulic Actuators,” IEEE Transactions on Control Systems Technology, vol. 21, no. 6, pp. 2417-2422, Nov. 2013.
[8] L. Consolini and C. M. Verrelli, ”Learning control in spatial coordinates for the path-following of autonomous vehicles,” Automatica, vol. 50, pp. 1867-1874, July 2014.
[9] R. A. Gupta and M. Y. Chow, ”Networked Control System: Overview and Research Trends,” IEEE Transactions on Industrial Electronics, vol. 57, no. 7, pp. 2527-2535, July 2010.
[10] L. Zhang, H. Gao and O. Kaynak, ”Network-Induced Constraints in Networked Control Systems—A Survey,” IEEE Transactions on Industrial Informatics, vol. 9, no. 1, pp. 403-416, Feb. 2013.
[11] D. Yue, Q. L. Han, and J. Lam, “Network-based robust H1 control of systems with uncertainty,” Automatica, vol. 41, no. 6, pp. 999-1007, Jun. 2005.
[12] F. Yang, Z. Wang, Y. S. Hung and M. Gani, ”H1 control for networked systems with random communication delays,” IEEE Transactions on Automatic Control, vol. 51, no. 3, pp. 511-518, Mar. 2006.
[13] Z. Shuai, H. Zhang, J. Wang, J. Li and M. Ouyang, ”Combined AFS and DYC Control of Four-Wheel-Independent-Drive Electric Vehicles over CAN Network with Time-Varying Delays,” IEEE Transactions on Vehicular Technology, vol. 63, no. 2, pp. 591-602, Feb. 2014.
[14] H. Gao and T. Chen, ”Network-Based H1 Output Tracking Control,” IEEE Transactions on Automatic Control, vol. 53, no. 3, pp. 655-667, April 2008.
[15] X. Jiang, Q. L. Han, S. Liu and A. Xue, ”A New H1 Stabilization Criterion for Networked Control Systems,” IEEE Transactions on Automatic Control, vol. 53, no. 4, pp. 1025-1032, May 2008.
[16] Z. Wang, F. Yang, D. W. C. Ho and X. Liu, ”Robust H1 Control for Networked Systems With Random Packet Losses,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 37, no. 4, pp. 916-924, Aug. 2007.
[17] F. Yang, Z. Wang, D. W. C. Ho and M. Gani, ”Robust H1 Control With Missing Measurements and Time Delays,” IEEE Transactions on Automatic Control, vol. 52, no. 9, pp. 1666-1672, Sept. 2007.
[18] J. Qiu, H. Gao and S. X. Ding, ”Recent Advances on Fuzzy-Model-Based Nonlinear Networked Control Systems: A Survey,” IEEE Transactions on Industrial Electronics, vol. 63, no. 2, pp. 1207-1217, Feb. 2016.
[19] K. Tanaka and H. O. Wang, Fuzzy Control Systems Design and Analysis: A Linear Matrix Inequality Approach. New York, NY, USA:Wiley, 2001.
[20] H. C. Sung D. W. Kim J. B. Park Y. H. Joo ”Robust digital control of fuzzy systems with parametric uncertainties: LMI-based digital redesign approach,” Fuzzy Sets Sys. vol. 161 pp. 919-933 2010
[21] C. S. Tseng C. K. Hwang ”Fuzzy observer-based fuzzy control design for nonlinear systems with persistent bounded disturbances,” Fuzzy Sets Syst. vol. 158 no. 2 pp. 164-179 Jan. 2007.
[22] W. Liu, C.-C. Lim, P. Shi, and S. Xu, ”Sampled-data fuzzy control for a class of nonlinear systems with missing data and disturbances,” Fuzzy Sets Syst., vol. 306, pp. 63-86, 2017.
[23] X. Jiang, ”On sampled-data fuzzy control design approach for T–S model-based fuzzy systems by using discretization approach,” Information Sciences, vol. 296, pp. 307-314, 2015.
[24] H. Liu and G. Zhou, ”Finite-time sampled-data control for switching T-S fuzzy systems,” Neurocomputing, vol. 166, pp. 294-300, 2015.
[25] B. S. Chen, W. H. Chen and H. L. Wu, ”Robust H2=H1 Global Linearization Filter Design for Nonlinear Stochastic Systems,” IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 56, no. 7, pp. 1441-1454, July 2009.
[26] B. S. Chen and C. F. Wu, ”Robust Scheduling Filter Design for a Class of Nonlinear Stochastic Poisson Signal Systems,” IEEE Transactions on Signal Processing, vol. 63, no. 23, pp. 6245-6257, Dec.1, 2015.
[27] B. Øksendal A. Sulem, Applied Stochastic Control of Jump Diffusions, 2nd ed. Berlin, Germany: Springer, 2007.
[28] F. Hanson, Applied Stochastic Processes and Control for Jump-Diffusions: Modeling Analysis and Computation, 2nd ed. Philadelphia, PA, USA:SIAM, 2007.
[29] F. L. Lewis, L. Xie D. Popa, Optimal and Robust Estimation: With an Introduction to Stochastic Control Theory. Boca Raton, FL, USA: CRC Press, 2008.
[30] D. P. Bertsekas and S. E. Shreve, Stochastic Optimal Control: The Discrete Time Case, vol. 139. New York, NY, USA: Academic, 1978.
[31] R. F. Stengel, Stochastic Optimal Control: Theory and Application. Hoboken, NJ, USA: Wiley 1986.
[32] C. Scherer, P. Gahinet and M. Chilali, ”Multiobjective output-feedback control via LMI optimization,” IEEE Transactions on Automatic Control, vol. 42, no. 7, pp. 896-911, Jul 1997.
[33] R. Skjetne and T. I. Fossen, ”Nonlinear maneuvering and control of ships,” MTS/IEEE Oceans 2001. An Ocean Odyssey. Conference Proceedings (IEEE Cat. No.01CH37295), Honolulu, HI, 2001, pp. 1808-1815 vol.3.
[34] S. H. ˙ Zak, Systems and Control, New York, NY, USA: Oxford Univ. Press, 2003.
[35] E. Fridman, ”A refined input delay approach to sampled-data control,” Automatica, vol. 46, pp. 421-427, 2010.
[36] E. Fridman, A. Seuret, and J.-P. Richard, ”Robust sampled-data stabilization of linear systems: an input delay approach,” Automatica, vol. 40, pp. 1441-1446, 2004.
[37] S. Boyd, L. El Ghaoui E. Feron, and V. Balakrishnan, Linear Matrix Inequalities in System and Control Theory. Philadelphia, PA, USA: SIAM 1994.
[38] B. S. Chen, H. C. Lee and C. F. Wu, ”Pareto Optimal Filter Design for Nonlinear Stochastic Fuzzy Systems via Multiobjective H2=H1 Optimization,” IEEE Transactions on Fuzzy Systems, vol. 23, no. 2, pp. 387-399, April 2015.
[39] B. Chen, C.-S. Tseng, and H.-J. Uang, ”Mixed H2=H1 Fuzzy Output Feedback Control Design for Nonlinear Dynamic Systems: An LMI Approach,” IEEE Transactions on Fuzzy Systems, vol. 8, no. 3, pp. 279-265, June 2000.
[40] K. Deb, Multi-Objective Optimization Using Evolutionary Algorithms, 1st ed. Chichester, U.K.:Wiley, 2001.
[41] A. Abraham L. C. Jain R. Goldberg, Evolutionary Multiobjective Optimization: Theoretical Advances and Applications. New York, NY, USA: Springer, 2005.
[42] K. Tanaka, T. Ikeda and H. O. Wang, ”Fuzzy regulators and fuzzy observers: relaxed stability conditions and LMI-based designs,” IEEE Transactions on Fuzzy Systems, vol. 6, no. 2, pp. 250-265, May 1998.
[43] M. Sugeno and K. Tanaka, ”Successive identification of a fuzzy model and its applications to prediction of a complex system,” Fuzzy Sets and Systems, vol. 42, pp. 315-334, Aug. 1991.
連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
無相關論文
 
無相關期刊
 
1. 隨機多無人機網路系統的強健追蹤控制在布朗和布瓦松雜訊干擾之下
2. 智慧電網中動態能量傳送系統之非合作與合作管理策略設計
3. 藉由大數具挖掘和NGS數據識別建構基因和表觀遺傳網路來探討乳頭狀甲狀腺癌的分子進展機制
4. 多蜂巢多使用者多輸入多輸出波束成型系統之多目標能量最小化設計
5. 藉大數據挖掘和全基因組辨別經由系統生物學方法來探究各期哮喘的進展機制
6. 對於非線性隨機跳躍擴散系統的模糊非合作賽局
7. 藉由大數據挖掘透過高通量數據和全基因組識別來研究大腸癌全基因組基因和表觀遺傳基因網路並探查其分子機制
8. 藉由大數據探勘、高通量資料估算與系統識別方法探討阿茲海默症病理機制
9. 藉由大數據挖掘和全基因組辨別來探究糖尿病前期到一型糖尿病的進展機制
10. 對於非線性隨機跳躍擴散系統的L_∞-gain模糊觀測器控制設計
11. 藉由大數據挖掘和全基因組識別建立基因與表觀遺傳網路 來探究OKF6/TERT-2 細胞與白色念珠菌SC5314 和WO-1 在感染過程的共同機制及藥物設計
12. 慧型控制系統應用於無人駕駛車
13. 多目標波束成形設計於多輸入單輸出正交分頻多工感知無線電系統
14. 具卜松跳躍之隨機平均場系統的多目標控制
15. 具有惡意攻擊之下的隨機跳躍擴散系統之多目標H2/H無窮估計方法的安全性增強濾波器設計