臺灣博碩士論文加值系統

本篇論文主要目的在於發展動態派翠遞迴式模糊類神經網路控制系統，並將其應用於自走車避障及路徑追蹤。動態派翠遞迴式模糊類神經網路中，將派翠網路和遞迴式架構的觀念引入傳統的模糊類神經網路，以減緩參數學習計算量的負擔以及增加網路對應的能力。首先，本論文採用監督式梯度遞減法來發展動態派翠遞迴式模糊類神經網路的線上調整法則，並且藉由離散型里亞普諾函數決定動態派翠遞迴式模糊類神經網路的學習速率以確保追蹤誤差收斂。為了更進一步強化系統的穩定性，本論文設計強健型動態派翠遞迴式模糊類神經網路控制系統，利用投影法則和里亞普諾穩定理論推導其網路參數調整法則，如此可確保網路參數收斂和系統穩定特性，並且不需要系統資訊以及補償的輔助控制器。本論文藉由自走車在不同路徑的數值模擬與實驗結果可以驗證所提出的路徑追蹤控制系統之有效性。
另一方面，自走車具有感測週遭環境的能力，藉由感測器的資訊可獲知車子與環境的相對位置，並且即時的規劃路徑到達目的地，因此，本論文亦設計一個適應性路徑規劃控制系統，其勿需事先瞭解環境資訊且無需龐大的記憶體空間以及冗餘的計算量；在此系統中，自走車可根據所設計之追蹤模組、避障模組、自旋模組以及狀態選擇逐漸的到達目的地，本論文並以自走車操縱在不同可能發生之障礙物形狀的數值模擬與實驗結果來驗證所設計之適應性路徑規劃控制系統之有效性。

This thesis focuses on the development of dynamic Petri recurrent fuzzy-neural-network (DPRFNN) control systems, and applies these designed control systems to the obstacle avoidance and path tracking of a nonholonomic mobile robot. In the DPRFNN, the concept of a Petri net (PN) and the recurrent frame of internal feedback loops are incorporated into a traditional fuzzy neural network (FNN) to alleviate the computation burden of parameter learning and to enhance the dynamic mapping of network ability. First, the supervised gradient descent method is used to develop the online training algorithm for the DPRFNN control, and analytical methods based on a discrete-type Lyapunov function are proposed to determine its varied learning rates for ensuring the convergence of path tracking errors. Moreover, a robust DPRFNN control system is designed to further enhance the system stability, and the corresponding adaptation laws of network parameters are established in the sense of projection algorithm and Lyapunov stability theorem to ensure the network convergence as well as stable control performance without the requirement of detailed system information and the compensation of auxiliary controllers. In addition, the effectiveness of the proposed path-tracking control schemes under different moving paths is verified by numerical simulations and experimental results.
On the other hand, the mobile robot is capable of sensing its surrounding environment, interpreting the sensed information to obtain the knowledge of its location and the environment, planning a real-time trajectory to reach the object. Thus, an adaptive path-planning control scheme is also designed without detailed environmental information, large memory size and heavy computation burden in this thesis for the obstacle avoidance of a mobile robot. In this scheme, the robot can gradually approach its object according to the motion tracking mode, obstacle avoidance mode, self-rotation mode, and robot state selection. The effectiveness of the proposed adaptive path-planning control scheme is verified by numerical simulations and experimental results of a differential-driving mobile robot under the possible occurrence of obstacle shapes.

書名頁 I
論文口試委員審定書 II
授權書 III
中文摘要 IV
Abstract VI
誌謝 VIII
Contents IX
List of Figures XII
List of Tables XVIII
Chapter 1 Introduction 1
Chapter 2 System Descriptions of Mobile Robot 7
2.1 Overview 7
2.2 Structure of mobile robot 8
2.2.1 Kinematic model of nonholonomic mobile robot 9
2.2.2 Dynamic model of nonholonomic mobile robot 11
2.3 Experimental equipment 12
Chapter 3 Dynamic Petri Recurrent Fuzzy-Neural-Network Control 14
3.1 Overview 14
3.2 DPRFNN control system 15
3.2.1 Dynamic Petri recurrent fuzzy-neural-network 15
3.2.2 Online learning algorithm 18
3.2.3 Convergence analyses 21
3.3 Experimental results 25
3.4 Conclusions 39
Chapter 4 Robust Dynamic Petri Recurrent Fuzzy-Neural-Network Control 41
4.1 Overview 41
4.2 Robust DPRFNN control system 42
4.2.1 Stabilizing control 42
4.2.2 Robust DPRFNN control 44
4.3 Numerical simulations and experimental results 51
4.3.1 Numerical simulations 52
4.3.2 Experimental results 65
4.4 Conclusions 67
Chapter 5 Adaptive Path-Planning Control 69
5.1 Overview 69
5.2 Adaptive path-planning control 69
5.2.1 Motion tracking mode 71
5.2.2 Obstacle avoidance mode 75
5.2.3 Self-rotation mode 77
5.2.4 Robot state selection 78
5.3 Numerical simulations and experimental results 80
5.3.1 Numerical simulations 80
5.3.2 Experimental results 83
5.4 Conclusions 84
Chapter 6 Discussions and Suggestions for Future Research 86
6.1 Discussions 86
6.2 Suggestions for future research 87
References 90
作者簡歷 96

[1]J. Palac?n, J. A. Salse, I. Valga??n, and X. Clua, “Building a mobile robot for a floor-cleaning operation in domestic environments,” IEEE Trans. Instrum. Meas., vol. 53, no. 5, pp. 1418-1424, 2004.
[2]D. Ding and R. A. Cooper, “Electric-powered wheelchairs: a review of current technology and insight into future direction,” IEEE Contr. Syst. Mag., vol. 25, no. 2, pp. 22-34, 2005.
[3]T. Yamaguchi, E. Sato, and Y. Takama, “Intelligent space and human centered robotics,” IEEE Trans. Ind. Electron., vol. 50, no. 5, pp. 881-889, 2003.
[4]J. M. Lee, K. Son, M. C. Lee, J. W. Choi, S. H. Han, and M. H. Lee, “Localization of a mobile robot using the image of a moving object,” IEEE Trans. Ind. Electron., vol. 50, no. 3, pp. 612-619, 2003.
[5]S. Y. Oh, J. H. Lee, and D. H. Choi, “A new reinforcement learning vehicle control architecture for vision-based road following,” IEEE. Trans. Vehicular Technol., vol. 49, no. 3, pp. 997-1005, 2000.
[6]T. C. Lee, C. Y. Tsai, and K. T. Song, “Fast parking control of mobile robots: a motion planning approach with experimental validation,” IEEE Trans. Contr. Syst. Technol., vol. 12, no. 5, pp. 661-676, 2004.
[7]L. X. Wang, A Course in Fuzzy Systems and Control, New Jersey: Prentice-Hall, 1997.
[8]H. Seraji and A. Howard, “Behavior-based robot navigation on challenging terrain: a fuzzy logic approach,” IEEE Trans. Robot. Automat., vol. 18, no. 3, pp. 308-321, 2002.
[9]S. X. Yang, H. Li, M. Q. H. Meng, and P. X. Liu, “An embedded fuzzy controller for a behavior-based mobile robot with guaranteed performance,” IEEE Trans. Fuzzy Syst., vol. 12, no. 4, pp. 436-446, 2004.
[10]T. H. S. Li, S. J. Chang, and W. Tong, “Fuzzy target tracking control of autonomous mobile robots by using infrared sensors,” IEEE Trans. Fuzzy Syst., vol. 12, no. 4, pp. 491-501, 2004.
[11]C. L. Hwang, L. J. Chang, and Y. S. Yu, “Network-based fuzzy decentralized sliding-mode control for car-like mobile robots,” IEEE Trans. Ind. Electron., vol. 54, no. 1, pp. 574-585, 2007.
[12]G. Antonelli, S. Chiaverini, and G. Fusco, “A fuzzy-logic-based approach for mobile robot path tracking,” IEEE Trans. Fuzzy Syst., vol. 15, no. 2, pp. 211-221, 2007.
[13]O. Omidvar and D. L. Elliott, Neural Systems for Control, Academic Press, 1997.
[14]S. X. Yang and M. Q. H. Meng, “Real-time collision-free motion planning of a mobile robot using a neural dynamics-based approach,” IEEE Trans. Neural Netw., vol. 14, no. 6, pp. 1541-1552, 2003.
[15]T. Das, I. N. Kar, and S. Chaudhury, “Simple neuron-based adaptive controller for a nonholonomic mobile robot including actuator dynamics,” Neurocomputing, vol. 69, no. 16-18, pp. 2140-2151, 2006.
[16]F. J. Lin, R. J. Wai, W. D. Chou, and S. P. Hsu, “Adaptive backstepping control using recurrent neural network for linear induction motor drive,” IEEE Trans. Ind. Electron., vol. 49, no. 1, pp. 134-146, 2002.
[17]R. J. Wai, C. M. Lin, and Y. F. Peng, “Adaptive hybrid control for linear piezoelectric ceramic motor drive using diagonal recurrent CMAC network,” IEEE Trans. Neural Netw., vol. 15, no. 6, pp. 1491-1506, 2004.
[18]S. J. Yoo, Y. H. Choi, and J. B. Park, “Generalized predictive control based on self-recurrent wavelet neural network for stable path tracking of mobile robots: adaptive learning rates approach,” IEEE Trans. Circuit Syst. I, vol. 53, no. 6, pp. 1381-1394, 2006.
[19]C. T. Lin and C. S. George Lee, Neural Fuzzy Systems, New Jersey: Prentice-Hall, 1996.
[20]F. J. Lin, R. J. Wai, and C. C. Lee, “Fuzzy neural network position controller for ultrasonic motor drive using push-pull DC-DC converter,” IEE Proc. Control Theory Appl., vol. 146, no. 1, pp. 99-107, 1999.
[21]C. Ye, N. H. C. Yung, and D. Wang, “A fuzzy controller with supervised learning assisted reinforcement learning algorithm for obstacle avoidance,” IEEE Trans. Syst., Man, Cybern. B, vol. 33, no. 1, pp. 17-27, 2003.
[22]F. J. Lin, R. J. Wai, K. K. Shyu, and T. M. Liu, “Recurrent fuzzy neural network control for piezoelectric ceramic linear ultrasonic motor drive,” IEEE Trans. Ultrason., Ferroelect., Freq. Cont., vol. 48, no. 4, pp. 900-913, 2001.
[23]R. J. Wai, “Total sliding-mode controller for PM synchronous servo motor drive using recurrent fuzzy neural network,” IEEE Trans. Ind. Electron., vol. 48, no. 5, pp. 926-944, 2001.
[24]R. David and H. Alla, “Petri nets for modeling of dynamic systems: A survey,” Automatica, vol. 30, no. 2, pp. 175-202, 1994.
[25]V. R. L. Shen, “Reinforcement learning for high-level fuzzy Petri nets,” IEEE Trans. Syst., Man, Cybern. B, vol. 33, no. 2, pp. 351-362, 2003.
[26]R. J. Wai and C. C. Chu, “Motion control of linear induction motor via Petri fuzzy-neural-network,” IEEE Trans. Ind. Electron., vol. 54, no. 1, pp. 281-295, 2007.
[27]T.-H. S. Li, S. J. Chang, and Y. X. Chen, “Implementation of human-like driving skills by autonomous fuzzy behavior control on an FPGA-based car-like mobile robot,” IEEE Trans. Ind. Electron., vol. 50, no. 5, pp. 867-880, 2003.
[28]W. Tsui, M. S. Masmoudi, F. Karray, I. Song, and M. Masmoudi, “Soft-computing-based embedded design of an intelligent wall/lane-following vehicle,” IEEE/ASME Trans. Mechatronics, vol. 13, no. 1, pp. 125-135, 2008.
[29]J. H. Lilly, “Evolution of a negative-rule fuzzy obstacle avoidance controller for an autonomous vehicle,” IEEE Trans. Fuzzy Syst., vol. 15, no. 4, pp. 718-728, 2007.
[30]Q. Li, W. Zhang, Y. Yin, Z. Wang, and G. Liu, “An improved genetic algorithm of optimum path planning for mobile robots,” Int. Conf. Intelligent Systems Design and Applications, vol. 2, pp. 637-642, 2006.
[31]J. Tu and S. Yang, “Genetic algorithm based path planning for a mobile robot,” IEEE Int. Conf. Robotics and Automation, pp. 1221-1226, 2003.
[32]Y. Hu and S. Yang, “A knowledge based genetic algorithm for path planning of a mobile robot,” IEEE Int. Conf. Robotics and Automation, pp. 4350-4355, 2004.
[33]W. Wu and Q. Ruan, “A gene-constrained genetic algorithm for solving shortest path problem,” Int. Conf. Signal Processing, pp. 2510-2513, 2004.
[34]J. Borenstein and Y. Koren, “The vector field histogram-fast obstacle avoidance for mobile robots,” IEEE Trans. Robot. Automat., vol. 7, no. 3, pp. 278-288, 1991.
[35]A. Zhu and S. X. Yang, “Neurofuzzy-based approach to mobile robot navigation in unknown environments,” IEEE Trans. Syst. Man, Cybern. C, vol. 37, no. 4, pp. 610-621, 2007.
[36]F. Amigoni and S. Gasparini, “Building segment-based maps without pose information,” Proc. IEEE, vol. 94, no. 7, pp. 1340-1359, 2006.
[37]G. L. Mariottini, G. Oriolo, and D. Prattichizzo, “Image-based visual servoing for nonholonomic mobile robots using epipolar genmetry,” IEEE Trans. Robotics, vol. 23, no. 1, pp. 87-100, 2007.
[38]M. Wang and J. N. K. Liu, “Fuzzy logic-based real-time robot navigation in unknown environment with dead ends,” Robot. Autonomous Syst., vol. 56, no. 7, pp. 625-643, 2008.
[39]J. Velagic, B. Lacevic, and B. Perunicic, “A 3-level autonomous mobile robot navigation system designed by using reasoning/search approaches,” Robot. Autonomous Syst., vol. 54, no. 12, pp. 989-1004, 2006.
[40]K. M. Krishna and P. K. Kalra, “Perception and remembrance of the environment during real-time navigation of a mobile robot,” Robot. Autonomous Syst., vol. 37, pp. 25-51, 2001.
[41]M. Wang and J. N. K. Liu, “Fuzzy logic based robot path planning in unknown environments,” Int. Conf. Machine Learning and Cybernetics, vol. 2, pp. 813-818, 2005.
[42]L. Yao and S. H. Zhang, “Design of path following and obstacle avoidance strategies for automatic guided vehicle,” Master Thesis, Dept. Electrical Engineering, National Taipei Univ. of Technology, Republic of China, 2003.
[43]H. Hardt, D. Wolf, and R. Husson, “The dead reckoning localization system of the wheeled mobile robot ROMANE,” IEEE Inter. Conf. Multisensor Fusion and Integration for Intelligent Systems, pp. 603-610, 1996.
[44]F. J. Lin, R. J. Wai, and C. M. Hong, “Identification and control of rotary travelling-wave type ultrasonic motor using neural networks,” IEEE Trans. Contr. Syst. Technol., vol. 9, no. 4, pp. 672-680, 2001.
[45]C. H. Lee and C. C. Teng, “Identification and control of dynamic systems using recurrent fuzzy neural network,” IEEE Trans. Fuzzy Syst., vol. 8, no. 4, pp. 349-366, 2000.
[46]F. Sun, Z. Sun, and P. Y. Woo, “Neural network-based adaptive controller design of robotic manipulators with an observer,” IEEE Trans. Neural Netw., vol. 12, no. 1, pp. 54-67, 2001.
[47]Y. G. Leu, W. Y. Wang, and T. T. Lee, “Observer-based direct adaptive fuzzy-neural control for nonaffine nonlinear systems,” IEEE Trans. Neural Netw., vol. 16, no. 4, pp. 853-861, 2005.
[48]S. Haykin, Neural Networks: A Comprehensive Foundation, New Jersey: Prentice-Hall, 1994.
[49]K. J. Astrom and B. Wittenmark, Adaptive Control, New York: Addison-Wesley, 1995.
[50]H. K. Khalil, Nonlinear Systems, New Jersey: Prentice-Hall, 1996.