本論文主要研究傳統(第一型)模糊控制器之實際應用與新型(第二型)模糊控制器之發展與應用。首先，針對具有全方位影像感測器之車型機器人，設計一模糊停車控制器。此模糊停車控制器是由全方位影像感測器找出停車格與車型機器人之相對位置，然後經由座標轉換及路徑規劃，完成自動停車之任務。其次，針對具立體視覺之輪型機器人，設計目標追蹤及障礙物規避之模糊控制器。其中立體視覺系統，結合了數種影像處理技術，找出環境中實際的目標物與障礙物，求出目標物與障礙物相對於移動式機器人的角度位置及實際距離，選擇不同的行為模式，並驅動移動式機器人在巡邏過程中閃避障礙物及追蹤目標。
接著，本論文提出第二型模糊免疫控制器。此控制器是整合第二型模糊邏輯系統與免疫系統的回饋機制設計而成，其中第二型模糊邏輯是用來近似免疫系統中的非線性函數，而輸出的歸屬函數是採用含有不確定性的單值函數以簡化運算過程的複雜度，並利用幾何分析的方式求解第二型模糊邏輯的明確輸出值。最後，將此區間式第二型模糊免疫控制器，應用於離散線性系統及倒單擺系統，由模擬結果顯示，此第二型模糊免疫控制器在系統有外在干擾的情況下仍具有可行性及有效性，且性能亦優於第一型模糊控制器、第二型模糊控制器與第一型模糊免疫控制器。

This dissertation focuses on the applications of type-1 fuzzy controller and the development of a new kind of fuzzy controller, the interval type-2 fuzzy immune controller. Firstly, an omni-directional vision-based control scheme for the car-like mobile robot (CLMR) is presented. From the image information, one can estimate the position of the CLMR in the parking space and figure out a feasible reference path. Then, we propose a fuzzy logical control to manipulate the steering wheel such that it can execute parallel-parking missions. On the other hand, an image processing approach for real-time target tracking and obstacle avoidance for mobile robot navigation in an indoor environment using stereo vision is also proposed. Several image processing techniques are combined to find the target and obstacles. Then one can compute the angular position of the detected target and obstacle related to the mobile robot. The stereo vision system is utilized to calculate the relative distance of the target and obstacle from the mobile robot. According to the distance, one can determine the relationship of the target and obstacle to the mobile robot. The best target tracking path and obstacle avoidance path can be determined by different behavior modes. Therefore, the mobile robot plans a collision-free and successful track target to complete the patrol routine.
Moreover, a novel interval type-2 fuzzy immune control (IT2FIC) for linear and nonlinear discrete systems is presented. The controller is designed by integration of the interval type-2 fuzzy logic control (IT2FLC) and immune feedback control law. The type-2 fuzzy logic system is adopted to approximate the undetermined nonlinear function of the immune system. In order to reduce the computational loads of the type-reduction process, singleton type membership function with uncertain width and a new algorithm is proposed for type-reduction with a geometric analysis. The simulation results of the discrete linear system and the inverted pendulum system demonstrate that the proposed IT2FIC can obtain the best tracking performance among the type-1 fuzzy logic control (T1FLC), the IT2FIC, and the type-1 fuzzy immune control (T1FIC).

[1] L. A. Zadeh, “The concept of a linguistic variable and its application to approximate reasoning–I,” Information Sciences, Vol. 8, pp. 199–249, 1975.
[2] M. Sugeno and K. Murakami, “An experimental study on fuzzy parking control using a model car,” Industrial Applications of Fuzzy Control, North-Holland, The Netherlands, pp. 105–124, 1985.
[3] M. Sugeno, T. Murofushi, T. Mori, T. Tatematsu, and J. Tanaka, “Fuzzy algorithmic control of a model car by oral instructions,” Fuzzy Sets Syst., Vol. 32, pp. 207–219, 1989.
[4] A. Ohata and M. Mio, “Parking control based on nonlinear trajectory control for low speed vehicles,” in Proc. IEEE Int. Conf. Industrial Electronics, pp. 107–112, 1991.
[5] S. Yasunobu and Y. Murai, “Parking control based on predictive fuzzy control,” in Proc. IEEE Int. Conf. Fuzzy Systems, Vol. 2, pp. 1338–1341, 1994.
[6] W. A. Daxwanger and G. K. Schmidt, “Skill-based visual parking control using neural and fuzzy networks,” in Proc. IEEE Int. Conf. System, Man, Cybernetics, Vol. 2, pp. 1659–1664, 1995.
[7] A. Tayebi and A. Rachid, “A time-varying-based robust control for the parking problem of a wheeled mobile robot,” in Proc. IEEE Int. Conf. Robotics and Automation, pp. 3099–3104, 1996.
[8] H. An, T. Yoshino, D. Kashimoto, M. Okubo, Y. Sakai, and T. Hamamoto, “Improvement of convergence to goal for wheeled mobile robot using parking motion,” in Proc. IEEE Int. Conf. Intelligent Robots Systems, pp. 1693–1698, 1999.
[9] M. Ohkita, H. Mitita, M. Miura, and H. Kuono, “Traveling experiment of an autonomous mobile robot for a flush parking,” in Proc. 2nd IEEE Conf. Fuzzy System, Vol. 2, pp. 327–332, 1993.
[10] D. Lyon, “Parallel parking with curvature and nonholonomic constraints,” in Proc. Symp. Intelligent Vehicles, Detroit, MI, pp. 341–346, 1992.
[11] I. E. Paromtchik and C. Laugire, “Motion generation and control for parking an autonomous vehicle,” in Proc. IEEE Conf. Robotics Automation, vol. 4, Minneapolis, MN, pp. 3117–3122, 1996.
[12] K. Y. Lian, C. S. Chin, and T. S. Chiang, “Parallel parking a car-like robot using fuzzy gain scheduling,” in Proc. IEEE Int. Conf. Control Applications, Vol. 2, pp. 1686–1691, 1999.
[13] K. Jiang, “A sensor guided parallel parking system for nonholonomic vehicles,” in Proc. IEEE Conf. Intelligent Transportation Systems, pp. 270–275, 2000.
[14] J. Xiu, G. Chen, and M. Xie, “Vision-guided automatic parking for smart car,” in Proc. IEEE Intelligent Vehicles Symp., pp. 725–730, 2000.
[15] D. Gorinevsky, A. Kapitanovsky, and A. Goldenberg, “Neural network architecture for trajectory generation and control of automated car parking,” IEEE Trans. Contr. Syst. Technol., Vol. 4, pp. 50–56, 1996.
[16] S. Lee, M. Kim, Y. Youm, and W. Chung, “Control of a car-like mobile robot for parking problem,” in Proc. IEEE Int. Conf. Robotics Automation, pp. 1–6, 1999.
[17] T.-H. S. Li and S. J. Chang, “Autonomous Fuzzy Parking Control of a Car-Like Mobile Robot,” IEEE Trans. on Systems, Man, and Cybernetics, Vol. 33, pp. 451–465, 2003.
[18] 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. on Industrial Electronics, Vol. 50, No.5, pp. 867–880, 2003.
[19] Y. Yagi, Y. Nishizawa, and M. Yachida, “Map-based navigation for a mobile robot with omnidirectional image sensor COPIS,” IEEE Trans. Robot. Automat., Vol. 11, No. 5, pp. 634–648, Oct. 1995.
[20] C. Pegard and E. M. Mouaddib “A mobile robot using a panoramic view,” in Proc. IEEE Int. Conf. on Robotics and Automation, pp. 89–94, 1996.
[21] N. Winters, J. Gaspar, Gerard Lacey and J. Santos, “Omni-directional vision for robot navigation,” in Proc. IEEE Workshop on Omnidirectional Vision, pp.21–28, 2000.
[22] J. Gaspar, N. Winters and J. Santos, “Vision-based navigation and enviromental representations with an omnidirectional camera,” IEEE Trans. on Robotics and Automation, Vol. 16, No. 6, pp. 890–898, 2000.
[23] I.. D. Scalbe, “Natural representations for straight lines and the Hough transform on discrete arrays,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 11, No. 9, pp. 941–950, 1989.
[24] D. P. Hunttenlocher, R. H. Lilien, and C. F. Olson, “View-based recognition using an eigenspace approximation to hausdorff measure,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 21, No. 9, pp. 951–955, 1999.
[25] X. Yi and I. Octavial, “Line-based recognition using a multidimensional hausdorff distance,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 21, No. 9, pp. 901–916, 1999.
[26] J. S. Chahl and M. V. Srinivasan, “A complete panoramic vision system, incorporating imaging, ranging, and three dimensional navigation,” in Proc. IEEE Workshop on Omnidirectional Vision, pp. 104–111, 2000.
[27] Pihsiang Machinery Mfg. Co., Ltd., http://www.pihsiang.com.tw/
[28] Micro-Star-Int’l Co. Ltd., http://www.msi.com.tw/
[29] R. C. Gonzales and R. E. Woods, Digital Image Processing, Second Edition, 2002.
[30] A. Elfes, “Using occupancy grid for mobile robot perception and navigation,” IEEE Computer Magazine, Vol. 22, No. 6, pp. 46–57, 1989.
[31] H. Li and S. X. Yang, “Ultrasonic sensor based fuzzy obstacle avoidance behaviors,” in Proc. IEEE Int. Conf. on System, Man and Cybernetics, Vol. 2, pp. 644–649, 2002.
[32] J. Hancock, M. Hebert, and C. Thorpe, “Laser intensity-based obstacle detection,” in Proc. IEEE/RSJ Int. Conf. on Intelligent Robotic Systems, Vol. 3, pp. 1541–1546, 1998.
[33] E. Menegatti, A. Pretto, A. Scarpa, and E. Pagello, “Omnidirectional vision scan matching for robot localization in dynamic environments,” IEEE Trans. on Robotics and Automation, Vol. 22, pp. 523–535, 2006.
[34] E. Elkonyaly, F. Areed, Y. Enab, and F. Zada, “Range sensory based robot navigation in unknown terrains,” in Proc. SPIE on the International Society for Optical Engineering, Vol. 2591, pp. 76–85, 1995.
[35] C. Lin and R. L. Tummala, “Adaptive sensor integration for mobile robot navigation,” in Proc. IEEE Int. Conf. on Multisensor Fusion and Integration for Intelligent System, pp. 85–91, Oct. 1994.
[36] K. Sugihara, “Some location problems for robot navigation using a single,” Computer Vision, Graphics and Image Processing, Vol. 42, No. 1, pp. 112–129, Apr. 1988.
[37] T. Tsubouchi and S. Yuta “Map assisted vision system of mobile robots for reckoning in a building environment,” in Proc. IEEE Int. Conf. on Robotics Automat., pp. 1978–1984, Mar./Apr. 1987.
[38] Y. Yagi, S. Kawato and S. Tsuji, “Collision avoidance using omnidirectional image sensor (COPIS),” in Proc. IEEE Int. Conf. on Robotics and Automation, Vol. 1, pp. 910–915, Apr. 1991.
[39] Y. Sun, Q. Cao, and W. Chen, “An object tracking and global localization method using omnidirectional vision system,” in Proc. 5th World Congress on Intelligent Control and Automation, Vol. 6, pp. 4730–4735, Jun. 2004.
[40] D. J. Kriegman, E. Triendl, and T. O. Binford, “Stereo vision and navigation in buildings for mobile robots,” IEEE Trans. on Robotics Automat., Vol. 5, No. 6, pp. 792–803, Dec. 1989.
[41] C. Caraffi, S. Cattani, and P. Grisleri, “Off-road path and obstacle detection using decision networks and stereo vision,” IEEE Trans. on Intelligent Transportation Systems, Vol. 8, pp. 607–618, 2007.
[42] N. Ayache and F. Lustman, “Trinocular stereo vision for robotics,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 13, No. 1, pp. 73–85, 1991.
[43] S. O. Lee, Y. J. Cho, M. H. Bo, B. J. You, and S. R. Oh, “A stable target tracking control for unicycle mobile robots,” in Proc. Int. Conf. on Intelligent Robots and Systems, Vol. 3, pp. 1822–1827, 2000.
[44] C. C. Wong, C. T. Cheng, K. H. Huang, and Y. T. Yang, “Fuzzy control of humanoid robot for obstacle avoidance,” International Journal of Fuzzy System, Vol. 10, No.1, pp. 261–270, 2008.
[45] T. Darrell, G. Gordon, M. Harville, and J. Woodfill, “Integrated person tracking using stereo, color and pattern detection,” in Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, pp. 601–608, Jun. 1998.
[46] J. Orwell, P. Remagnino, and G. A. Jones, “Multi-camera colour tracking,” in IEEE Workshop on Visual Surveillance, pp. 14–21, Jul. 1999.
[47] T.-H. S. Li, S. J. Chang, and W. Tong, “Fuzzy target tracking control of autonomous mobile robots by using infrared sensors,” IEEE Trans. on Fuzzy Syst. Vol. 12, No. 4, pp. 491–501, 2004.
[48] C. C. Wong, H. Y. Wang, and S. A. Li, “PSO-based motion fuzzy controller design for mobile robots,” International Journal of Fuzzy Systems, Vol. 10, No. 1, pp. 284–292, Mar. 2008.
[49] R. C. Luo and T. M. Chean, “Autonomous mobile target tracking system based on gray-fuzzy control algorithm,” IEEE Trans. on Ind. Electron. Vol. 47, No. 4, pp. 920–931, 2000.
[50] S. F. Lin, S. S. Liao, and J. Y. Chen, “Design of fuzzy predictor in the moving target system,” International Journal of Fuzzy Systems, Vol. 3, No. 1, pp. 323–330, 2001.
[51] N. S. Pai and T.-H. S. Li, “Tracking 3D moving targets with an integrated fuzzy Kalman filter,” International Journal of Fuzzy Systems, Vol. 5, No. 4, pp. 201–211, 2003.
[52] Y. Y. Chen and K.Y. Young, “An Intelligent Radar Predictor for High-Speed Moving-Target Tracking,” International Journal of Fuzzy Systems, Vol. 6, No.2, pp. 90–99, 2004.
[53] C. C. Wong, H. Y. Wang, S. A. Li, and C. T. Cheng, “Fuzzy controller designed by GA for two-wheeled mobile robots,” International Journal of Fuzzy Systems, Vol. 9, No.1, pp.22–30, 2007.
[54] J. Lu, H. Cai, J. G. Lou, and Jiang Li, “An Epipolar Geometry-Based Fast Disparity Estimation Algorithm for Multiview Image and Video Coding,” IEEE Trans. on Circuits and Systems for Video Technology, Vol. 17, No. 6, pp. 737–750, 2007.
[55] D. Culibrk, O. Marques, D. Socek, H. Kalva, and B. Furht, “Neural network approach to background modeling for video object segmentation,” IEEE Trans. Neural Networks, Vol. 18, No. 6, pp. 1614–1627, 2007.
[56] B. Linfeng, C. Fugui, and Z. Xiangjin, “Fuzzy adaptive proportional integral and differential with modified smith predictor for micro assembly visual servoing,” Information Technology Journal, Vol. 8, No. 6, pp. 195–201, 2009.
[57] Z. Li, Y. Li and X. Duan, “Improved Strength Pareto Evolutionary Algorithm with Local Search Strategies for Optimal Reactive Power Flow,” Information Technology Journal, Vol. 9, No. 4, pp. 749–757, 2010.
[58] L. Yu, , Z. Cai, Z. Jiang, and Hu Q, “An advanced fuzzy immune PID-type tracking controller of a nonholonomic mobile robot,” in Proc. of IEEE Inter. Conf. on Automation and Logistics, pp. 66–71, 2007.
[59] G. C. Luh and W. W. Liu, “An immunological approach to mobile robot reactive navigation,” Applied Soft Computing. Vol. 8, pp. 30–45, 2008.
[60] K. Takahashi and T. Yamada, “Application of an immune feedback mechanism to control systems,” JSME International Journal, Series C, Vol. 41, No. 2, pp. 184–191, 1998.
[61] T.-H. S. Li, S. H. Tsai, J. Z. Lee, M. Y. Hsiao, and C.H. Chao, “Robust fuzzy control for a class of uncertain discrete fuzzy bilinear systems,” IEEE Trans. Syst., Man, Cybern. Part B, Vol. 38, No. 2, pp. 510–527, 2008.
[62] M. Y. Hsiao, T.-H. S. Li, J. Z. Lee, C. H. Chao, and S. H. Tsai, “Design of interval type-2 fuzzy sliding-mode controller,” Information Sciences, Vol. 178, No. 6, pp. 1696–1716, 2008.
[63] M. Y. Hsiao, C. Y. Chen, and T.-H. S. Li, “Interval type-2 adaptive fuzzy sliding-mode dynamic control design for wheeled mobile robots,” International Journal of Fuzzy Systems, Vol.10, No. 4, pp. 268–275, 2008.
[64] N. N. Karnik and J. M. Mendel, “Introduction to Type-2 fuzzy logic systems,” Proc. IEEE FUZZ Conf., Anchorage AK, pp. 915–920, 1998.
[65] N. N. Karnik, J. M. Mendel and Q. Liang, “Type-2 fuzzy logic systems,” IEEE Trans. Fuzzy Systems, Vol. 7, pp. 643–658, 1999.
[66] Q. Liang and J. M. Mendel, “Interval type-2 fuzzy logic systems: Theory and design,” IEEE Transactions on Fuzzy Systems, Vol. 8, pp. 535–550, 2000.
[67] D. Wu and J. M. Mendel, “Uncertainty measures for interval type-2 fuzzy sets,” Information Sciences, Vol. 177, No. 23, pp. 5378–5393, 2007.
[68] Z. Dianyou, W. Shitong, H. Bin and H. Dewen, “A class of new fuzzy inference systems with linearly parameter growth and without any rule base,” Information Technology Journal, Vol. 6, No. 5, pp. 704–710, 2007.
[69] J. Kan, W. Li, and J. Liu, “Fuzzy immune self-tuning PID controller and its simulation,” in Proc. of IEEE Inter. Conf. on Industrial Electronics and Applications, pp. 625–628, 2008.
[70] L. X. Wang and J. M. Mendel, “Fuzzy basis functions, universal approximation, and orthogonal least-squares learning,” IEEE Trans. on Neural Networks, Vol. 3, No. 5, pp. 807–814, 1992.
[71] A. Visconti and H. Tahayori, “A type-2 fuzzy set recognition algorithm for artificial immune systems,” Lecture Notes in Artificial Intelligence, Vol. 5271, pp. 491–498, 2008.
[72] L. V. Fausett, Applied Numerical Analysis Using MATLAB, Prentice Hall, 2007.
[73] H. O. Wang, K. Tanaka, and M. F. Griffin, “An approach to fuzzy control of nonlinear systems: stability and design issues,” IEEE Trans. on Fuzzy System, Vol. 4, No. 1, pp. 14–23. 1996.
[74] L. Astudillo, O. Castillo, and L. T. Aguilar, “Intelligent control for a perturbed autonomous wheeled mobile robot: A type-2 fuzzy logic approach,” Journal of Nonlinear Studies, Vol. 14, No. 3, pp. 37–48, 2007.
[75] G. Bartolini and A. Ferrara, “Multi-input sliding mode control of a class of uncertain nonlinear system,” IEEE Tran. on Automatic Control, Vol. 41, No. 11, pp. 1662–1667, 1996.
[76] J. M. Mendel, R. I. John, and F. Liu, “Interval type-2 fuzzy logic systems made simple,” IEEE Trans. on Fuzzy Systems, Vol. 14, No. 6, pp. 808–821, 2006.
[77] J. M. Mendel and R. I. John, “Type-2 fuzzy sets made simple,” IEEE Trans. on Fuzzy Systems, Vol. 10, No. 2, pp. 117–127, 2002.