臺灣博碩士論文加值系統

近年來，由於電腦資訊與通訊科技日新月異，其應用在各型交通運輸工具之自動偵測、控制等技術上，以確保車輛性能之充分發揮及行車安全之提昇，並可以將車內、外之各種資訊作充分之溝通，形成所謂之智慧型車輛。
本篇論文主要針對小腦模型控制器進行設計，並結合適應控制、監督式控制及遞迴控制設計技巧，並應用於閉迴路系統的控制上。在監督式遞迴小腦模型控制器設計上，汽車追隨控制問題可視為一個追蹤問題，利用監督式遞迴小腦模型控制器來控制汽車追隨系統可獲得相當優異的控制效能。最後，將發展多輸入多輸出的迴授式小腦模型控制器，並應用於變換車道系統的控制上。經由模擬的結果顯示，對於這些系統，本論文所提出的控制器均能獲得滿足的控制性能。除了使用matlab進行模擬外，同時也利用虛擬實境(VR)技術進行模擬，在VR端系統，我們利用3D Studio MAX建構場景，並用新的3D動畫開發工具MATFOR VR來製作整個動作流程，並且在場景中加入系統動態數學函式及控制方法，並藉由此虛擬實境來呈現車輛的平移及旋轉動作。

The application of new technologies of computer and communications on the transportation vehicle can improve the vehicle safety and accelerate the vehicle performance dramatically. Also, the vehicle can communicate with all other information suppliers at any time. This is called the Intelligent Vehicle (IV).
This thesis focuses on the design of the cerebellar model articulation controller (CMAC) based on adaptive control, supervisory control and recursive control, which attempt to provide a comprehensive treatment of CMACs in closed-loop control applications. For supervisory recurrent cerebellar-model articulation controller (SRCMAC), the car-following control system is formulated as a tracking problem. The SRCMAC is designed to achieve satisfactory tracking performance for car-following control system. Finally, a design method of recurrent CMAC for multi-input multi-output nonlinear systems is developed and is applied to lane-change control system. From the simulation results, the proposed intelligent control techniques have been shown to achieve satisfactory control performance for the considered nonlinear systems. In addition to use matlab’s simulations, the virtual reality (VR) simulations are also carried out. In the VR system, we use 3D Studio MAX to construct the scenes, and use the 3D animation development tool MATFOR VR to program the entire playing process. Moreover, we add dynamic motion equation and control method into the scenes, and use virtual reality technique to show the motion of translation and rotation of vehicles.

Contents
摘要 i
Abstract iii
誌謝 v
Contents vi
List of Figure viii
Nomenclature xii
1. Introduction
1.1 General Remark and Overview of Previous Work 1
1.2 Organization of This Thesis 4
2. Cerebellar Model Articulation Controller (CMAC)
2.1 Overview 5
2.2 Original Cerebellar Model Articulation Controller 6
2.3 General Cerebellar Model Articulation Controller 8
3. Car-Following Control Using Supervisory Recurrent Cerebellar Model Articulation Controller
3.1 Overview 17
3.2 Problem Formulation 18
3.2.1 Platoon dynamic 18
3.2.2 Vehicle model 18
3.3 Car-Following Control System Design 20
3.3.1 Description of RCMAC 20
3.4 Supervisory Controller 22
3.5 On-line Parameter Training Algorithm 24
3.6 Simulation Results 25
3.7 Summary 27
4. Lane-Change Control Using Recurrent Cerebellar Model Articulation Controller
4.1 Overview 39
4.2 MIMO Nonlinear Systems 40
4.3 RCMAC Network 42
4.4 RCMAC Design 45
4.5 Stabilizing Adaptive Laws for RCMAC 47
4.6 Illustrative Example 51
4.7 Summary 54
5. Conclusions and Suggestions for Future Research
5.1 Conclusions 71
5.2 Suggestions for Future Research 72
Reference 73
Autobiography 79

Reference
[1] K. S. Narendra and K. Parthasara thy , “Identification and control of dynamical systems using neural networks,” IEEE Trans. Neural Networks, vol. 1, no. 1, pp. 4-27, 1990.
[2] R. M. Sanner and J.-J. E. Slotine, “Gaussian networks for direct adaptive control,” IEEE Trans. Neural Networks, vol. 3, no. 6, pp. 837-863, 1992.
[3] A. Agarwal, “ A systematic classification of neural-network-based control,” IEEE Contr. Syst. Mag., vol. 17, pp. 75-93, 1997.
[4] F. L. Lewis, A. Yesildirek, and K. Liu, “Multilayer neural-net robot controller with guaranteed tracking performance,” IEEE Trans. Neural Networks, vol. 7, no. 2, pp. 388-399, 1996.
[5] K, Nam, “Stabilization of feedback linearizable systems using radial basis function network,” IEEE Trans. Automatic Control, vol. 44, no. 5, pp. 1026-1031, 1999.
[6] S. S. Ge, C. C. Hang, and T. Zhang, “Adaptive neural network control of nonlinear systems by state and output feedback,” IEEE Trans. Systems, Man, and Cybernetics-Part B: Cybernetics, vol. 29, no. 6, pp. 818-828, 1999.
[7] J. R. Noriega and H. Wang, “A direct adaptive neural-network control for unknown nonlinear systems and its application,” IEEE Trans. Neural Networks, vol. 9, pp. 27-34, 1998.
[8] M. Zhihong, H. R. Wu, and M. Palaniswami, “An adaptive tracking controller using neural networks for a class of nonlinear systems,” IEEE Trans. Neural Networks, vol. 9, no. 5, pp. 947-1031, 1998.
[9] M. A. Mayosky and G. I. E Cancelo, “Direct adaptive control of wind energy conversion system using Gaussian network,” IEEE Trans. Neural Networks, vol. 10, pp. 898-906, 1999.
[10] C. C. Ku and K. Y. Lee, “Diagonal recurrent neural networks for dynamic systems control,” IEEE Trans. Neural Networks, vol. 6, no. 1, pp. 144-156, 1995.
[11] T. W. S. Chow and Y. Fang, “A recurrent neural-network-based real-time learning control strategy applying to nonlinear systems with unknown dynamics,” IEEE Trans. Ind. Electron., vol. 45, no. 1, pp. 151-161, 1998.
[12] C. Gan and K. Danai, “Model-based recurrent neural network for modeling nonlinear dynamic systems,” IEEE Trans. Systems, Man, and Cybernetics- Part B: Cybernetics, vol. 30, no. 2, pp. 344-351, 2000.
[13] Y. Fang, T. W. S. Chow, and X. D. Li, “Use of a recurrent neural network in discrete sliding-mode control,” IEE Proc., Control Theory and Applications, vol. 146, no. 1, pp. 84-90, 1999;
[14] J. S. Albus, “A new approach to manipulator control: The cerebellar model articulation controller (CMAC),” J. Dyn. Syst., Measurement, Contr., vol. 97, pp. 220-227, 1975.
[15] M. Brown, C. J. Harris, and P. C. Parks, “The interpolation capabilities of the binary CMAC”, Neural Network, vol. 6, pp. 429-440, 1993.
[16] S. H. Lane, D. A. Handelman, and J. J. Gelfand, “Theory and development of higher-order CMAC neural networks,” IEEE Contr. Syst. Mag., vol. 12, no. 2, pp. 23-30, 1992.
[17] F. J. Gonzalez-Serrano, A. R. Figueiras-Vidal, and A. Artes-Rodriguez, “Generalizing CMAC neural networks,” IEEE Trans. Neural Networks, vol. 9, pp. 1509-1514, 1998.
[18] S. Fabri and V. Kadirkamanathan, “Dynamic structure neural networks for stable adaptive control of nonlinear systems,” IEEE Trans. Neural Networks, vol. 7, pp. 1151-1166, 1996.
[19] S. Seshagiri and H. K. Khalil, “Output feedback control of nonlinear systems using RBF neural networks,” IEEE Trans. Neural Networks, vol. 11, pp. 69-79, 2000.
[20] C. T. Chiang and C. S. Lin, “Integration of CMAC technique and weighted regression for efficient learning and output differentiability,” IEEE Trans. Syst., Man, Cybern., pt. B, vol. 28, no. 2, pp. 231-237, 1998.
[21] H. Shiraishi, S. L. Ipri, and D. D. Cho, “CMAC neural network controller for fuel-injection systems,” IEEE Trans. Contr. Syst. Technol., vol. 3,no. 2, pp. 32-38, 1995.
[22] K. S. Hwang and C. S. Lin, “Smooth trajectory tracking of three-link robot: a self-organizing CMAC approach,” IEEE Trans. Syst., Man, Cybern. B, vol. 28, no. 5, pp. 680-692, 1998.
[23] R. J. Wai, C. M. Lin and Y. F. Peng, “Robust CMAC neural network control for LLCC-resonant driving linear piezoelectric ceramic motor,” IEE Proc., Contr. Theory Appl., vol. 150, no. 3, pp. 221-232, 2003.
[24] D. Marr, “A theory of cerebellar cortex,” J. Physiol., vol. 202, pp. 437-470, 1969.
[25] D. O. Hebb, The organization of Behavior: A Neuropsychological Theory. New York: Wiley, 1949.
[26] M. Ito, “Mechanisms of motor learning in the cerebellum,” Brain Research Interactive, vol. 886, pp. 237-245, 2000.
[27] S. Kikuchi, and P. Chrkroborty, “Car-following model based on fuzzy inference system”, Transportation Res. Rec. 1365, TRB, 1993.
[28] W. Lang, C. Wang, and Y. Chiang, “On the car-following model with fuzzy control”, Proc. 2nd Nat. Conf. Fuzzy Theory & Application (Fuzzy’94), pp. 380—385,1994.
[29] R. J. Caudill, and W. L. Garrard, “Vehicle-follower longitudinal control for automated transit vehicles”, Journal of Dynamic Systems, Measurement and Control, Vol. 99, No. 4, pp. 241-248, 1997.
[30] H. Y. Chiu, G. B. Stupp, Jr., and S. J. Brown, J., “Vehicle-follower control with variable gains for short headway automated guideway transit systems”, Journal of Dynamic Systems, Measurement and Control, Vol. 99, No. 3, pp. 183-189, 1997.
[31] S. Sheikholeslam, and C. A. Desoer, “ A system level study of the longitudinal control of a platoon of vehicles”, Journal of Dynamic Systems, Measurement and Control, Vol. 114, No. 2, pp. 286-292, 1992.
[32] S. Sheikholeslam, and C. A. Desoer, “Longitudinal control of a platoon of vehicles with no communication of lead vehicle information: A system level study”, IEEE Transactions on Vehicular Technology , Vol. 42, No 4, pp. 546-554, 1990.
[33] L. X. Wang, Adaptive Fuzzy Systems and Control: Design and Stability Analysis, Englewood Cliffs, NJ: Prentice-Hall, 1994.
[34] S. Sastry and M. Bodson, Adaptive Control — Stability, Convergence, and Robustness, Englewood Cliffs, NJ: Prentice-Hall, 1989.
[35] S. Sastry and M. Bodson, Adaptive control — stability, convergence, and robustness, Prentice-Hall, Englewood Cliffs, New Jersey, pp. 19-22, 1998.
[36] J. Godthelp, A. R. A. Van Der Horst, S. Burrij, and C. Van Der Lagemaat, “Open and closed loop steering in a lane change maneuver,” Institute for Perception: National Defense Research Organization Group, 1983.
[37] R. A. Hess and A. Modjtahedzadeh, “A preview control model of driver steering behavior,” 1989 IEEE International Conference on Systems, Man and Cybernetics, vol. 2, pp. 504-509, 1989.
[38] R. Rajesh, H. S. Tan, B. K. Law, and W. B. Zhang, “Demonstration of integrated longitudinal and lateral control for the operation of automated vehicles in platoons,” IEEE Transactions on Control System Technology, vol. 8, no. 4, pp. 695-708, 2000.
[39] D. N. Godbole, V. Hagenmeyer, R. Sengupta, and D. Swaroop, “Design of emergency for automated highway system: Obstacle avoidance problem,” IEEE Conference on Decision and Control, vol. 5, pp. 4774-4779, 1997.
[40] Z. Shiller and S. Sunder, “Emergency lane-change maneuvers of autonomous vehicles,” ASME J. Dynamic Systems, Measurement, and Control, vol. 120, no. 1, pp. 37-44, 1998.
[41] T. Takagi and M. Sugeno, “Fuzzy identification of systems and its applications to modeling and control,” IEEE Trans. Syst., Man, Cybern. B, vol. 15, pp116-132, 1985.
[42] J. S. Albus, “A new approach to manipulator control: The cerebellar model articulation controller (CMAC),” J. Dyn. Syst., Measur., Contr., vol. 97, pp. 220-227, 1975.
[43] S. H. Lane, D. A. Handelman, and J. J. Gelfand, “Theory and development of higher-order CMAC neural networks, “ IEEE Contr. Syst. Mag., vol. 12, pp. 23-30, 1992
[44] Y. H. Kim and F. L. Lewis, “Optimal design of CMAC neural-network controller for robot manipulators,” IEEE Trans. Syst., Man, Cybern., C, vol. 30, pp. 22-31, 2000.
[45] C. T. Chiang and C. S. Lin, “CMAC with general basis functions, ” Neural Netw., vol. 9, pp. 1199-1211, 1996.
[46] S. Jagannathan, “Discrete-time CMAC NN control of feedback linearizable nonlinear systems under a persistence of excitation,” IEEE Trans. Neural Netw., vol. 10, pp. 128-137, 1999.
[47] S. G. Cao, N. W. Rees, and G. Feng, “Stability analysis of fuzzy control systems,” IEEE Trans. Syst., Man, Cybern. B, vol. 26, no. 1, pp. 201-204, 1996.
[48] C. C. Lee, “Fuzzy logic in control system: fuzzy logic controller-part Ⅰ/Ⅱ,” IEEE Trans. Syst., Man, Cybern. B, vol. 20, no. 2, pp. 404-435, 1990.
[49] Qiuzhen Qu, Jianwu Zhang, and Yanzhu Liu, “Variable structure model following control of active four-wheel-steering vehicle based on the optimal reference model,” Vehicle Electronics Conference, 1999. (IVEC ''99) Proceedings of the IEEE International, vol. 1, pp. 254-257, 1999.