跳到主要內容

臺灣博碩士論文加值系統

(18.97.9.169) 您好!臺灣時間:2025/02/09 21:54
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:蔡忠庭
研究生(外文):Zhong-Ting Cai
論文名稱:前置雷達電動車之自優化適應性巡航控制器
論文名稱(外文):Design of Self-Optimizing Adaptive Cruise Controller for Radar-Guided Electric Vehicle
指導教授:林巍聳
指導教授(外文):Wei-Song Lin
口試委員:鍾鴻源廖德誠施慶隆張國維
口試日期:2016-07-13
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:電機工程學研究所
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:2016
畢業學年度:104
語文別:中文
論文頁數:126
中文關鍵詞:電動車適應性巡航控制模糊PID控制器適應最佳控制自主優化
外文關鍵詞:Electric vehicleAdaptive cruise controlFuzzy PID controllerAdaptive optimal controlSelf-optimizing controller
相關次數:
  • 被引用被引用:0
  • 點閱點閱:124
  • 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:1
適應性巡航控制(Adaptive Cruise Control, ACC)系統是實現自動駕駛的一項關鍵技術,車輛搭載ACC系統可以代替駕駛操作反覆的車速控制,因此可以減輕駕駛的注意力和視力負荷,然而現有的ACC系統存在下列幾個問題:1. 無法適用於各種駕駛情境;2. 車間距離過大無法達到提升道路吞吐量的目的,且容易面臨車輛插隊(Cut-in)的情境;3. 頻繁的加減速導致消耗額外的能量。
本論文以使用輪轂直流無刷馬達為動力的電動車為研究對象,設想毫米波雷達、動力總成、車輛等規格和參數齊備,用適應最佳控制演算法結合模糊PID控制器構成ACC系統的自優化適應性巡航控制器,以和前車的間隔時間(Headway time)和碰撞時間的倒數(Inverse Time To Collision, ITTC)做為模糊邏輯的前件部輸入,適應最佳控制演算法以優化跟車性能和能源效率為目標,透過不同的試驗行駛自動優化各模糊規則之下的PID控制器參數。
本論文用電腦模擬驗證ACC系統的自優化適應性巡航控制器,透過US06行車型態及多種情境的訓練自動優化各模糊規則的PID參數,訓練完成的控制器在UDDS、HWFET、US06、LA92行車型態,以及車輛插隊、停再開(Stop & Go)、追上前車時進行減速並跟隨前車的模擬情境下都能達成目標,且結果顯示使用自優化適應性巡航控制器和模糊邏輯控制器相比,皆能以較小的累計誤差及較高的能源效率完成任務。

Adaptive cruise control (ACC) system is a key component of automotive autopilot. It assists the driver with automatic speed control that lessens driver’s burden in attention and vision. However, several shortcomings appear in the existing ACC system. First, the system is not applicable to many driving scenarios that the driver may encounter. Second, a large headway distance is necessary that diminishes road throughput and may incur cut-in situations. Third, frequent acceleration and deceleration makes the vehicle consume more energy. This thesis presents the self-optimizing adaptive cruise controller that can maintain vehicle speed under various driving resistance, follow leading car even in stop-and-go, and prevent from any collision while a car cut-in. This innovative design is developed on an in-wheel motor-powered electric vehicle that has a front millimeter-wave radar to detect leading car’s relative speed and distance. The self-optimizing adaptive cruise controller consists of a fuzzy PID controller and the adaptive optimal control (AOC) algorithm. Particularly, premise inputs are headway time and inverse time-to-collision (ITTC). Fuzzification actually divides the operating points of the vehicle system into several linear regions, each associated with a PID control law. The AOC algorithm is dedicated to adjust the PID parameters for achieving better cruising performance and energy efficiency. On a simulation system, the proposed design is examined in driving cycles such as UDDS, HWFET, US06, and LA92, including scenarios such as cut-in, stop & go, and car following. The self-optimizing adaptive cruise controller succeeds in every driving cycle and scenario, and outperforms a fuzzy logic controller in term of accumulated error and energy efficiency.

口試委員會審定書........................................... i
誌謝 ..................................................... ii
中文摘要 ................................................ iii
Abstract.................................................. iv
目錄 ..................................................... vi
圖目錄 ................................................... ix
表目錄 .................................................. xii
第一章 緒論 ............................................... 1
1.1 適應性巡航控制 .................................. 1
1.1.1 適應性巡航控制簡介 ....................... 1
1.1.2 適應性巡航控制的發展史 ................... 2
1.2 研究動機與文獻回顧 .............................. 5
1.2.1 研究動機 ................................. 5
1.2.2 適應性巡航控制器設計文獻回顧 ............. 6
1.3 章節介紹 ....................................... 10
第二章 前置雷達之輪轂馬達電動車 .......................... 11
2.1 車用雷達 ....................................... 11
2.2 輪轂馬達電動車 ................................. 16
2.3 車輛行駛阻力 ................................... 23
2.4 適應性巡航控制系統及電動車加速度之數學模型 ..... 29
2.4.1 電動車加速度之數學模型 .................. 29
2.4.2 適應性巡航控制之數學模型 ................ 29
第三章 前置雷達電動車之自優化適應性巡航控制器............. 31
3.1 李亞普諾夫理論 ................................. 32
3.1.1 李亞普諾夫理論簡介 ...................... 32
3.1.2 李亞普諾夫穩定性定理 .................... 32
3.2 PID控制器....................................... 33
3.2.1 PID控制器簡介............................ 33
3.3 模糊理論 ....................................... 36
3.3.1 模糊理論簡介 ............................ 36
3.3.2 模糊化 .................................. 37
3.3.3 模糊知識庫 .............................. 39
3.3.4 模糊推理引擎 ............................ 42
3.3.5 解模糊化 ................................ 42
3.4 適應最佳控制 ................................... 43
3.4.1 最佳控制簡介 ............................ 43
3.4.2 最佳控制之必要條件 ...................... 46
3.4.3 適應最佳控制之自優化策略 ................ 48
3.5 輻狀基底函數類神經網路 ......................... 54
3.5.1 類神經網路簡介 .......................... 54
3.5.2 類神經網路的分類 ........................ 56
3.5.3 輻狀基底函數類神經網路 .................. 59
3.6 前置雷達電動車之自優化適應性巡航控制器 ......... 63
3.6.1 速度控制器設計 .......................... 65
3.6.2 模糊PID控制器............................ 70
3.6.3 模糊PID控制器之自優化策略................ 75
第四章 電腦模擬與驗證 .....................................80
4.1 適應性巡航控制系統之模擬架構 ................... 80
4.1.1 行車型態測試簡介 ........................ 81
4.1.2 訓練程序 ................................ 84
4.2 適應性巡航控制系統之巡航性能分析 ............... 90
4.2.1 傳統巡航控制之性能分析 .................. 90
4.2.2 適應性巡航控制之性能分析 ................ 91
4.2.3 速度控制器對環境參數變動之性能分析...... 109
4.3 適應性巡航控制系統之能源效率分析 .............. 111
4.3.1 UDDS行車型態測試........................ 112
4.3.2 HWFET行車型態測試....................... 114
4.3.3 US06行車型態測試........................ 117
4.3.4 LA92行車型態測試........................ 119
4.3.5 巡航性能及能源效率分析總結 ............. 122
第五章 結論及未來展望 ................................... 123
5.1 結論 .......................................... 123
5.2 未來展望 ...................................... 123
參考文獻 .................................................124

[1] W. Prestl, T. Sauer, J. Steinle, and O. Tschernoster, "The BMW Active Cruise Control ACC," SAE Technical Paper, no. 2000-01-0344, 2000.
[2] M. Schneider, "Automotive radar–Status and trends," in Proc. German Microwave Conference, pp. 144-147, 2005.
[3] A. Vahidi and A. Eskandarian, "Research advances in intelligent collision avoidance and adaptive cruise control," IEEE Transactions on Intelligent Transportation Systems, vol. 4, pp. 143-153, 2003.
[4] J. Wagner, T. Baker, G. Goodin, and J. Maddox, "Automated vehicles: Policy implications scoping study," Texas A&M Transportation Institute, Texas A&M University, Research Report SWUTC/14/600451-00029-1, 2014.
[5] S. Shladover, "Review of the state of development of advanced vehicle control systems (AVCS)," Vehicle Syst. Dyn., vol. 24, pp. 551-595, July 1995.
[6] R. L. French, Y. Noguchi, and K. Sakamoto, "International competitiveness in IVHS: Europe, Japan, and the United States," in Vehicle Navigation and Information Systems Conference, 1994. Proceedings., Yokohama, Japan, pp. 525-530, July 1994.
[7] F. Broqua, G. Lerner, V. Mauro, and E. Morello, "Cooperative driving: basic concepts and a first assessment of "intelligent cruise control" strategies," in Proc. Advanced Telematics in Road Transport, vol. 11, DRIVE Conference, Brussels, Belgium, pp. 908-929, Feb 1991.
[8] M. Persson, F. Botling, E. Hesslow, and R. Johansson, "Stop and go controller for adaptive cruise control," in Proceedings of the 1999 IEEE Int. Conf. on Control Applications and IEEE Int. Symp. Computer-Aided Control System Design, pp. 1692-1697, 1999.
[9] Y. Kyongsu, M. Ilki, and K. Young Do, "A vehicle-to-vehicle distance control algorithm for stop-and-go cruise control," in IEEE intelligent transportation systems conference proceedings, pp. 478-482, 2001.
[10] S. Moon, I. Moon, and K. Yi, "Design, tuning, and evaluation of a full-range adaptive cruise control system with collision avoidance," Control Engineering Practice, vol. 17, pp. 442-455, 2009.
[11] G. J. L. Naus, J. Ploeg, M. J. G. Van de Molengraft, W. P. M. H. Heemels, and M. Steinbuch, "Design and implementation of parameterized adaptive cruise control: An explicit model predictive control approach," Control Engineering Practice, vol. 18, pp. 882-892, 2010.
[12] J. Ploeg, B. T. M. Scheepers, E. van Nunen, N. van de Wouw, and H. Nijmeijer,"Design and experimental evaluation of cooperative adaptive cruise control," in 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 260-265, 2011.
[13] C. C. Chan and K. T. Chau, Modern Electric Vehicle Technology: Oxford University Press, 2001.
[14] M. Ehsani, Y. Gao, and A. Emadi, Modern Electric, Hybrid Electric, and Fuel Cell Vehicles: Fundamentals, Theory, and Design, Second Edition: CRC Press, 2010.
[15] K. El Majdoub, F. Giri, H. Ouadi, L. Dugard, and F. Z. Chaoui, "Vehicle longitudinal motion modeling for nonlinear control," Control Engineering Practice, vol. 20, pp. 69-81, 2012.
[16] B. Ganji, A. Z. Kouzani, S. Y. Khoo, and M. Shams-Zahraei, "Adaptive cruise control of a HEV using sliding mode control," Expert Systems with Applications, vol. 41, pp. 607-615, 2/1/ 2014.
[17] C.-C. Tsai, S.-M. Hsieh, and C.-T. Chen, "Fuzzy Longitudinal Controller Design and Experimentation for Adaptive Cruise Control and Stop&Go," Journal of Intelligent & Robotic Systems, vol. 59, pp. 167-189, 2010.
[18] Q. Chengqun, "A design of automobile cruise control system based on fuzzy PID," in International Conference on Information Science, Electronics and Electrical Engineering (ISEEE), pp. 450-452, 2014.
[19] W.-S. Lin, "Optimality and convergence of adaptive optimal control by reinforcement synthesis," Automatica, vol. 47, pp. 1047-1052, 2011.
[20] J. Wenger, "Automotive radar - status and perspectives," in IEEE Compound Semiconductor Integrated Circuit Symposium, 2005. CSIC ''05, p. 4, 2005.
[21] J. Hasch, E. Topak, R. Schnabel, T. Zwick, R. Weigel, and C. Waldschmidt, "Millimeter-Wave Technology for Automotive Radar Sensors in the 77 GHz Frequency Band," IEEE Transactions on Microwave Theory and Techniques, vol. 60, pp. 845-860, 2012.
[22] B. E. GmbH, "Bosch Engineering Long-Range-Radar LRR3: Radar sensor for railway applications," http://www.bosch-engineering.de, June 6, 2016, 2010.
[23] A. Watts, A. Vallance, A. Whitehead, C. Hilton, and A. Fraser, "The Technology and Economics of In-Wheel Motors," SAE Int. J. Passeng.Cars-Electron. Elect. Syst., vol. 3, no. 2, pp. 37-57, 2010.
[24] N. Hashemnia and B. Asaei, "Comparative study of using different electric motors in the electric vehicles," in Electrical Machines, 2008. ICEM 2008. 18th International Conference on, pp. 1-5, 2008.
[25] M. Anderson and D. Harty, "Unsprung Mass with In-Wheel Motors - Myths and Realities," AVEC 10, UK, 2010.
[26] L.Evans and J.MacIsaac, "NHTSA Tire Fuel Efficiency Consumer Information Program Development: Phase 2 –Effects of Tire Rolling Resistance Levels on Traction, Treadwear, and Vehicle Fuel Economy," United States, 2009.
[27] T. D. Gillespie, Fundamentals of Vehicle Dynamics: Society of Automotive Engineers, 1992.
[28] A. A. o. S. Highway and T. Officials, A Policy on Geometric Design of Highways and Streets, 2001: American Association of State Highway and Transportation Officials, 2001.
[29] C. Bohn and D. P. Atherton, "An analysis package comparing PID anti-windup strategies," IEEE Control Systems, vol. 15, pp. 34-40, 1995.
[30] A. S. Hodel and C. E. Hall, "Variable-structure PID control to prevent integrator windup," IEEE Transactions on Industrial Electronics, vol. 48, pp. 442-451, 2001.
[31] A. Visioli, "Modified anti-windup scheme for PID controllers," IEE Proceedings - Control Theory and Applications, vol. 150, pp. 49-54, 2003.
[32] L. A. Zadeh, "Fuzzy sets," Information and Control, vol. 8, pp. 338-353, 1965/06/01 1965.
[33] R. Kruse, J. E. Gebhardt, and F. Klowon, Foundations of Fuzzy Systems: John Wiley & Sons, Inc., 1994.
[34] T. Takagi and M. Sugeno, "Fuzzy identification of systems and its applications to modeling and control," IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-15, pp. 116-132, 1985.
[35] M. Sugeno and G. T. Kang, "Structure identification of fuzzy model," Fuzzy Sets and Systems, vol. 28, pp. 15-33, 1988/10/01 1988.
[36] F. L. Lewis and V. L. Syrmos, Optimal control: John Wiley & Sons, 1995.
[37] 張晉棠, "適應最佳控制為基礎之工業控制系統循序優化技術," 電機工程學研究所, 國立臺灣大學, 2011.
[38] 戴念儒, "電動車高效率牽引系統之自優化模糊PID控制器設計," 電機工程學研究所, 國立臺灣大學, 2013.
[39] W.-S. Lin and C.-H. Zheng, "Constrained adaptive optimal control using a reinforcement learning agent," Automatica, vol. 48, pp. 2614-2619, 2012.
[40] 鄭陳鴻, "燃料電池混合電動車之能量管理優化技術," 電機工程學研究所, 國立臺灣大學, 2012.
[41] S. Haykin, Neural Networks: A Comprehensive Foundation: Prentice Hall PTR, 1998.
[42] P. V. Yee and S. Haykin, Regularized radial basis functional networks: theory and applications: John Wiley & Sons, Inc., 2001.

QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top