模糊控制應用於機器人系統

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姓名

黃正豪(Cheng-Hao Huang) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

模糊控制應用於機器人系統
(Fuzzy Control Applications to Robotic Systems)

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摘要(中)

本論文目的為模糊控制的設計與實現應用於機器人系統。本文將提出兩個獨立的模糊控制架構，分別運用於兩輪倒單擺自走車(two-wheel inverted pendulum, TWIP)和機器手臂系統(robot arm system)。首先，針對兩輪倒單擺自走車所提出的模糊控制架構包含四個模糊控制器，分別為模糊站立平衡控制器(fuzzy balanced standing control, FBSC)、模糊定速行進控制器(fuzzy constant velocity control, FCVC)、模糊移動定位控制器(fuzzy traveling and position control, FTPC)及模糊車身轉向控制器(fuzzy yaw steering control, FYSC)。根據兩輪車的T-S模糊模型(T-S fuzzy model)，建立對應的平行分散補償器(parallel distributed compensator, PDC)以達到兩輪車的站立平衡控制。再基於兩輪車的運動特性，可利用Mamdani型態的模糊規則庫(Mamdani-type if-then fuzzy rule base)描述並建立兩輪車的定速行進控制、移動定位控制、車身轉向控制。完整的模糊控制架構將以嵌入式設計於系統可程式化晶片(system-on-a-programmable-chip, SoPC)上的軟核心處理器(soft-core processor)實現兩輪車的控制。電腦模擬的結果將說明控制器設計概念，而實際實驗結果呈現此模糊控制架構對於兩輪車的控制效能。再者，機器手臂系統之目的在於實現物體抓取控制(object grasping control)。利用標準的逆運動學(inverse-kinematics, IK)的觀念操控機器手臂的運動，再運用雙攝影機(two-CCD)視覺回授量測機器手臂位置誤差，使用模糊規則庫去描述並建立模糊位置誤差補償器(fuzzy position error compensator, FPEC)，以調整機器手臂的定位點進而縮減位置誤差，使得機器手臂可精準到達目標位置。最後，有效抓取區域(effective grasping region)的觀念則配合模糊位置誤差補償器，使得機器手掌得以成功抓取目標物體。實驗結果將驗證控制架構對於機器手臂系統的可行性。整體而言，對於建構實體的雙輪自走車和機器手臂系統，所需要使用到的硬體與軟體技術都將在本論文做說明。

摘要(英)

This dissertation introduces the design and implementation of fuzzy controls on robotic applications including a two-wheel inverted pendulum (TWIP) system and a robot arm system. Two fuzzy control schemes are proposed for the TWIP and the robot arm, respectively. First, the control scheme for the TWIP includes four kinds of fuzzy controls which are fuzzy balanced standing control (FBSC), fuzzy constant velocity control (FCVC), fuzzy traveling and position control (FTPC), and fuzzy yaw steering control (FYSC). Based on the Takagi-Sugeno (T-S) fuzzy model of the TWIP, a parallel distributed compensator (PDC) is constructed as the FBSC. Based on the motion characteristic of the TWIP, the FCVC, FTPC, and FYSC are designed in terms of Mamdani-type if-then fuzzy rule bases (FRBs). Then the fuzzy control scheme is embedded into a system-on-a-programmable-chip (SoPC) developmental soft-core processor to implement the controls of TWIP. Computer simulations are given to illustrate the control design ideas and practical experiments are conducted to demonstrate the effectiveness of the fuzzy control scheme for the TWIP. In addition, the concerned control for the robot arm is to realize the object grasping behavior. The standard inverse-kinematics (IK) technique is utilized to manipulate the robot arm. Based on the two-CCD visual sensory feedback, an FRB is proposed as fuzzy position error compensator (FPEC) to adjust the robot gripper position and to reduce the position error, such that the gripper can accurately reach a target position. The concept of effective grasping region is further presented to collaborate with FPEC such that the robot arm can grasp a target object precisely. Experimental results are exemplified to verify the feasibility of the control scheme for the robot arm. In summary, the requisite hardware and software techniques are introduced to establish a real TWIP and a robot arm system.

關鍵字(中)

★ 模糊控制
★ 機器人控制
★ 嵌入式系統
★ 兩輪倒單擺自走車
★ 機器手臂

關鍵字(英)

★ Robot Arm
★ Embedded System
★ Fuzzy Control
★ Two-Wheel Inverted Pendulum (TWIP)
★ Robot Control

論文目次

摘要 I
Abstract II
致謝 III
Contents IV
List of Figures VII
List of Tables X
Chapter 1 Introduction 1
1.1 Background and Objectives 1
1.2 Review of Previous Works 2
1.2.1 Related works for the two-wheel inverted pendulum 2
1.2.2 Related works for the robot arm 4
1.3 Organization of the Dissertation 6
Chapter 2 System Implementation and Modeling of a Two-Wheel Inverted Pendulum 7
2.1 Introduction 7
2.2 Hardware Architecture 7
2.2.1 Hardware construction 8
2.2.2 FPGA-based SoPC development board 11
2.3 Mathematical Modeling 11
2.3.1 Nonlinear model 12
2.3.2 Fuzzy model 13
2.4 Summary 15
Chapter 3 Fuzzy Control Design for the Two-Wheel Inverted Pendulum 16
3.1 Introduction 16
3.2 Fuzzy Balanced Standing Control 16
3.2.1 FBSC design 16
3.2.2 Simulation results of balanced standing with FBSC 18
3.3 Fuzzy Constant Velocity Control 21
3.3.1 Constant velocity moving with FBSC 21
3.3.2 FCVC design 23
3.3.3 Simulation results of FCVC 30
3.4 Fuzzy Traveling and Position Control 31
3.4.1 Traveling and position motion with FBSC 32
3.4.2 FTPC design 33
3.4.3 Simulation results of FTPC 38
3.5 Fuzzy Yaw Steering Control 41
3.6 Summary 43
Chapter 4 Control Program Design and Experiments of the Two-Wheel Inverted Pendulum 44
4.1 Introduction 44
4.2 Control Program Design 44
4.3 Experimental Results 45
4.3.1 Experimental results of balanced standing control 46
4.3.2 Experimental results of constant velocity moving control 47
4.3.3 Experimental results of traveling and position control 53
4.3.4 Experimental results of yaw steering control 53
4.4 Summary 55
Chapter 5 System Description of a Robot Arm 56
5.1 Introduction 56
5.2 Hardware Architecture 56
5.2.1 Robot arm 56
5.2.2 Two-CCD vision device 58
5.2.3 Control center 58
5.3 Two-CCD Imaging Measurements 58
5.3.1 Single-CCD imaging geometry 59
5.3.2 Two-CCD imaging geometry 61
5.3.3 Coordinate transformation 62
5.4 Summary 62
Chapter 6 Fuzzy Control Design for the Robot Arm 63
6.1 Introduction 63
6.2 Inverse Kinematics Based Motion Control 63
6.3 Fuzzy Position Error Compensator 66
6.4 Fuzzy Position Control 68
6.5 Fuzzy Object Grasping Control 70
6.6 Summary 72
Chapter 7 Control Experiments of the Robot Arm 73
7.1 Introduction 73
7.2 Experimental Results 73
7.2.1 Experimental results of the fuzzy position control 73
7.2.2 Experimental results of fuzzy object grasping control 75
7.3 Summary 78
Chapter 8 Conclusion and Future Works 79
8.1 Conclusion 79
8.2 Future Works 79
Appendix 81
A.1 User IP Modules 81
A.2 Kalman Filter 84
References 85

參考文獻

[1] L. A. Zadeh, “Outline of a new approach to the analysis of complex systems and decision processes,” IEEE Trans. Syst. Man Cybern., vol. SMC-3, no. 1, pp. 28–44, Jan. 1973.
[2] M. Baloh and M. Parent, “Modeling and model verification of an intelligent self-balancing two-wheeled vehicle for an autonomous urban transportation system,” in Proc. Conf. Comput. Intell. Robot. Auton. Syst., Singapore, Dec. 2003.
[3] C.-H. Chiu, “The design and implementation of a wheeled inverted pendulum using an adaptive output recurrent cerebellar model articulation controller,” IEEE Trans. Ind. Electron., vol. 57, no. 5, pp. 1814–1822, May 2010.
[4] M. Fiacchini, A. Viguria, R. Cano, A. Prieto, F. R. Rubio, J. Aracil, and C. Canudas-de-Wit, “Design and experimentation of a personal pendulum vehicle,” in Proc. 7th Portuguese Conf. Autom. Control, Portugal, Sep. 2006.
[5] F. Grasser, A. D’Arrigo, S. Colombi, and A. C. Rufer, “JOE: a mobile, inverted pendulum,” IEEE Trans. Ind. Electron., vol. 49, no. 1, pp. 107–114, Feb. 2002.
[6] Y.-S. Ha and S. Yuta, “Trajectory tracking control for navigation of the inverse pendulum type self-contained mobile robot,” Robot. Auton. Syst., vol. 17, pp. 65–80, 1996.
[7] T. Takei, R. Imamura, and S. Yuta, “Baggage transportation and navigation by a wheeled inverted pendulum mobile robot,” IEEE Trans. Ind. Electron., vol. 56, no. 10, pp. 3985–3994, Oct. 2009.
[8] S. Jeong and T. Takahashi, “Wheeled inverted pendulum type assistant robot: design concept and mobile control,” Intell. Serv. Robot., vol. 1, no. 4, pp. 313–320, May 2008.
[9] S. Jung and S. S. Kim, “Control experiment of a wheel-driven mobile inverted pendulum using neural network,” IEEE Trans. Control Syst., vol. 16, no. 2, pp. 297–303, Mar. 2008.
[10] M. A. Karkoub, “Modelling and robust μ-synthesis control of an intelligent self balancing two-wheel vehicle,” Proc. Inst. Mech. Eng. Part K-J. Multi-Body Dyn., vol. 220, no. 4, pp. 293–302, Dec. 2006.
[11] Y. Kim, S. H. Kim, and Y. K. Kwak, “Dynamic analysis of a nonholonomic two-wheeled inverted pendulum robot,” J. Intell. Robot. Syst., vol. 44, no. 1, pp. 25–46, Sep. 2005.
[12] Y. Kim, S. H. Kim, and Y. K. Kwak, “Improving driving ability for a two-wheeled inverted-pendulum-type autonomous vehicle,” Proc. Inst. Mech. Eng. Part D-J. Automob. Eng., vol. 220, no. 2, pp. 165–175, Feb. 2006.
[13] Z. Li and J. Luo, “Adaptive robust dynamic balance and motion controls of mobile wheeled inverted pendulums,” IEEE Trans. Control Syst. Technol., vol. 17, no. 1, pp. 233–241, Jan. 2009.
[14] S. W. Nawawi, M. N. Ahmad, and J. H. S. Osman, “Real-time control of a two-wheeled inverted pendulum mobile robot,” in Proc. World Acad. Sci. Eng. Technol., May 2008, vol. 29, pp. 214–220.
[15] K. Pathak, J. Franch, and S. K. Agrawal, “Velocity and position control of a wheeled inverted pendulum by partial feedback linearization,” IEEE Trans. Robot., vol. 21, no. 3, pp. 505–513, Jun. 2005.
[16] A. Salerno and J. Angeles, “A new family of two-wheeled mobile robots: modeling and controllability,” IEEE Trans. Robot., vol. 23, no. 1, pp. 169–173, Feb. 2007.
[17] C.-C. Tsai, H.-C. Huang, and S.-C. Lin, “Adaptive neural network control of a self-balancing two-wheeled scooter,” IEEE Trans. Ind. Electron., vol. 57, no. 4, pp. 1420–1428, Apr. 2010.
[18] T. S. Hall and J. O. Hamblen, “System-on-a-programmable-chip development platforms in the classroom,” IEEE Trans. Educ., vol. 47, no. 4, pp. 502–507, Nov. 2004.
[19] H.-C. Huang and C.-C. Tsai, “FPGA implementation of an embedded robust adaptive controller for autonomous omnidirectional mobile platform,” IEEE Trans. Ind. Electron., vol. 56, no. 5, pp. 1604–1616, May 2009.
[20] S. Jung and S. s. Kim, “Hardware implementation of a real-time neural network controller with a DSP and an FPGA for nonlinear systems,” IEEE Trans. Ind. Electron., vol. 54, no. 1, pp. 265–271, Feb. 2007.
[21] Y.-S. Kung, R.-F. Fung, and T.-Y. Tai, “Realization of a motion control IC for X-Y table based on novel FPGA technology,” IEEE Trans. Ind. Electron., vol. 56, no. 1, pp. 43–53, Jan. 2009.
[22] 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, Oct. 2003.
[23] T.-H. S. Li, Y.-C. Yeh, J.-D. Wu, M.-Y. Hsiao, and C.-Y. Chen, “Multifunctional intelligent autonomous parking controllers for carlike mobile robots,” IEEE Trans. Ind. Electron., vol. 57, no. 5, pp. 1687–1700, May 2010.
[24] S. Sanchez-Solano, A. J. Cabrera, I. Baturone, F. J. Moreno-Velo, and M. Brox, “FPGA implementation of embedded fuzzy controllers for robotic applications,” IEEE Trans. Ind. Electron., vol. 54, no. 4, pp. 1937–1945, Aug. 2007.
[25] H. Tanaka, K. Ohnishi, H. Nishi, T. Kawai, Y. Morikawa, S. Ozawa, and T. Furukawa, “Implementation of bilateral control system based on acceleration control using FPGA for multi-DOF haptic endoscopic surgery robot,” IEEE Trans. Ind. Electron., vol. 56, no. 3, pp. 618–627, Mar. 2009.
[26] K. Furuta, K. Kosuge, and N. Mukai, “Control of articulated robot arm with sensory feedback: Laser beam tracking system,” IEEE Trans. Ind. Electron., vol. 35, no. 1, pp. 31–39, Feb. 1988.
[27] R. B. White, R. K. Read, M. W. Koch, and R. J. Schilling, “A graphics simulator for a robot arm,” IEEE Trans. Educ., vol. 32, no. 4, pp. 417–429, Nov. 1989.
[28] S. R. Munashnghe, M. Nakamura, S. Goto, and N. Kyura, “Optimum contouring of industrial robot arms under assigned velocity and torque constraints,” IEEE Trans. Syst. Man Cybern. Part C-Appl. Rev., vol. 31, no. 2, pp. 159–167, May 2001.
[29] C. W. Kennedy and J. P. Desai, “Modeling and control of the Mitsubishi PA-10 robot arm harmonic drive system,” IEEE-ASME Trans. Mechatron., vol. 10, no. 3, pp. 263–274, Jun. 2005.
[30] M. O. Efe, “Fractional fuzzy adaptive sliding-mode control of a 2-DOF direct-drive robot arm,” IEEE Trans. Syst. Man Cybern. Part B-Cyben., vol. 38, no. 6, pp. 1561–1570, Dec. 2008.
[31] W. Shen, J. Gu, and E. E. Milios, “Self-configuration fuzzy system for inverse kinematics of robot manipulators,” in Proc. NAFIPS Annu. meeting N. Am., Montreal, QC, Canada, Jun. 2006, pp. 41–45.
[32] V. Feliu, J. A. Somolinos, and A. Garcia, “Inverse dynamics based control system for a three-degree-freedom flexible arm,” IEEE Trans. Robot. Autom., vol. 19, no. 6, pp. 1007–1014, Dec. 2003.
[33] G. Antonelli, S. Chiaverini, and G. Fusco, “A new on-line algorithm for inverse kinematics of robot manipulators ensuring path tracking capability under joint limits,” IEEE Trans. Robot. Autom., vol. 19, no. 1, pp. 162–167, Feb. 2003.
[34] M. Shimizu, H. Kakuya, W.-K. Yoon, K. Kitagaki, and K. Kosuge, “Analytical inverse kinematics computation for 7-DOF redundant manipulators with joint limits and its application to redundancy resolution,” IEEE Trans. Robot., vol. 24, no. 5, pp. 1131–1142, Oct. 2008.
[35] K. Tchoń, “Optimal extended Jacobian inverse kinematics algorithms for robotic manipulators,” IEEE Trans. Robot., vol. 24, no. 6, pp. 1440–1445, Dec. 2008.
[36] W.-J. Wang, C.-H. Huang, I.-H. Lai, and H.-C. Chen, “A robot arm for pushing elevator buttons,” in Proc. SICE Annu. Conf., Taipei, Taiwan, Aug. 2010, pp. 1844–1848.
[37] A. Nilsson and P. Holmberg, “Combining a stable 2-D vision camera and an ultrasonic range detector for 3-D position estimation,” IEEE Trans. Instrum. Meas., vol. 43, no. 2, pp. 272–276, Apr. 1994.
[38] J.-Y. Baek and M.-C. Lee, “A study on detecting elevator entrance door using stereo vision in multi floor environment,” in Proc. ICROS-SICE Int. Joint Conf., Fukuoka, Japan, Aug. 2009, pp. 1370–1373.
[39] K. Okada, M. Kojima, S. Tokutsu, T. Maki, Y. Mori, and M. Inaba, “Multi-cue 3D object recognition in knowledge-based vision-guided humanoid robot system,” in Proc. IEEE/RSJ Int. Conf. Intell. Robot. Syst., San Diego, CA, USA, Oct. 2007, pp. 3217–3222.
[40] C. S. Fraser and S. Cronk, “A hybrid measurement approach for close-range photogrammetry,” ISPRS J. Photogramm. Remote Sens., vol. 64, no. 3, pp. 328–333, May 2009.
[41] F. A. van den Heuvel, “3D reconstruction from a single image using geometric constraints,” ISPRS J. Photogramm. Remote Sens., vol. 53, no. 6, pp. 354–368, Dec. 1998.
[42] D.-H. Zhang, J. Liang, and C. Guo, “Photogrammetric 3D measurement method applying to automobile panel,” in Proc. 2nd Int. Conf. Comput. Automat. Eng. (ICCAE), Feb. 2010, pp. 70–74.
[43] Z.-W. Gao, W.-K. Lin, Y.-S. Shen, C.-Y. Lin, and W.-C. Kao, “Design of signal processing pipeline for stereoscopic cameras,” IEEE Trans. Consum. Ellectron., vol. 56, no. 2, pp. 324–3331, May 2010.
[44] T. Egami, S. Oe, K. Terada, and T. Kashiwagi, “Three dimensional measurement using color image and movable CCD system,” in Proc. 27th Annu. Conf. IEEE Ind. Electron. Soc., Denver, CO, USA, Nov. 2001, pp. 1932–1936.
[45] C.-C. Hsu, M.-C. Lu, W.-Y. Wang, and Y.-Y. Lu, “Three-dimensional measurement of distant objects based on laser-projected CCD images,” IET Sci. Meas. Technol., vol. 3, no. 3, pp. 197–207, May 2009.
[46] C.-C. Hsu, M.-C. Lu, W.-Y. Wang, and Y.-Y. Lu, “Distance measurement based on pixel variation of CCD image,” ISA Trans., vol. 48, no. 4, pp. 389–395, Oct. 2009.
[47] C.-C. J. Hsu, M.-C. Lu, and Y.-Y. Lu, “Distance and angle measurement of objects on an oblique plane based on pixel number variation of CCD images,” IEEE Trans. Instrum. Meas., vol. 60, no. 5, pp. 1779–1794, May 2011.
[48] S. Das and N. Ahuja, “A comparative study of stereo, vergence, and focus as depth cues for active vision,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (CVPR ‘93), Jun. 1993, pp. 194–199.
[49] J. J. Aguilar, F, Torres, and M. A. Lope, “Stereo vision for 3D measurement: accuracy analysis, calibration and industrial applications,” Measurement, vol. 18, no. 4, pp. 193–200, Aug. 1996.
[50] E. Kim and H. Lee, “New approaches to relaxed quadratic stability condition of fuzzy control systems,” IEEE Trans. Fuzzy Syst., vol. 8, no. 5, pp. 523–534, Oct. 2000.
[51] K. Tanaka and H. O. Wang, Fuzzy Control Systems Design and Analysis: A Linear Matrix Inequality Approach. New York: Wiley, 2001.
[52] 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, Aug. 2004.
[53] C.-Y. Chang, “Adaptive fuzzy controller of the overhead cranes with nonlinear disturbance,” IEEE Trans. Ind. Inform., vol. 3, no. 2, pp. 164–172, May 2007.
[54] L. Sciavicco and B. Siciliano, Modelling and Control of Robot Manipulators. London: Springer, 2000.
[55] G. Welch and G. Bishop, An introduction to the Kalman filter. Chapel Hill, NC: Dept. Comput. Sci., Univ. North Carolina at Chapel Hill.

指導教授

王文俊(Wen-June Wang)

審核日期

2012-7-31

推文