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研究生:黃品瑄
研究生(外文):Pin-Hsuan Huang
論文名稱:以全域滑動模式控制為基礎之智慧型運動控制系統設計
論文名稱(外文):Total Sliding Mode Control Based Intelligent Motion Control System Design
指導教授:涂世雄涂世雄引用關係
指導教授(外文):Shih-Hsiung Twu
學位類別:碩士
校院名稱:中原大學
系所名稱:電機工程研究所
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:2009
畢業學年度:97
語文別:英文
論文頁數:88
中文關鍵詞:適應性控制全域滑動模式控制模糊類神經網路
外文關鍵詞:adaptive controlFuzzy neural networktotal sliding mod
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本論文之主旨係在於發展智慧型運動控制系統,其中利用模糊類神經網路為近似基礎並結合適應性控制、全域滑動模式控制與強健控制等理論;再依據李雅普諾夫穩定性定理來設計模糊類神經網路控制器的參數適應性調整法則;使得整個閉迴路控制系統的穩定性可以被保證。本論文首先將介紹滑動模式控制、全域滑動模式控制及模糊類神經網路的基本概念。接著將推導滑動模式控制、全域滑動模式控制及適應性全域滑動模式控制等方法於非線性運動控制系統的設計步驟。最後將其應用於船舶航向運動控制、機翼震盪運動控制等系統當中。然而,以上的方法皆存在著不理想的控制量抖動。因此,本論文最後提出以全域滑動模式控制為基礎之智慧型運動控制系統來解決此問題。在此控制方法當中,適應性模糊類神經網路控制器為主要控制器,並用來近似理想全域滑動模式控制律;而適應性強健控制器則用來消除模糊類神經網路與理想全域滑動模式控制律之近似誤差。最後,將滑動模式控制、全域滑動模式控制、適應性全域滑動模式控制與所提出來具PI-type適應法則之智慧型運動控制系統作一系列的比較。經由模擬結果證實;所提出來的智慧型運動控制系統的控制性能比起其它控制方法而言;將可達到令人滿意的控制性能,且沒有控制量的抖動現象。
The purpose of this thesis is to develop the intelligent motion control system based on the fuzzy neural network (FNN), and to integrate it with adaptive control, total sliding mode control (TSMC) and robust control technologies for motion control systems. According to Lyapunov synthesis approach, the adaptive tuning laws of FNN can be derived and the system stability can be guaranteed. This thesis introduces the concepts of sliding mode control (SMC), TSMC and FNN first. Then, the design procedures of SMC, TSMC and adaptive TSMC (ATSMC) are developed for the nonlinear motion control systems; that include a ship motion control system and a wing rock motion control system. However, the undesired control chattering also existed in the above control methods. To resolve the problem, this thesis proposes the TSMC-based intelligent motion control system for the same motion control systems. In this design, the adaptive FNN controller acts as the main tracking controller, which is designed via an FNN to mimic an ideal TSMC law and the adaptive robust controller is developed to attenuate the approximation error between the FNN and the ideal TSMC. Finally, comparisons of a SMC, a TSMC, an ATSMC and the proposed TSMC-based intelligent control method for motion control systems are made. The simulation results demonstrate that the proposed FNN and TSMC-based intelligent motion control scheme with PI-type adaptive laws can achieve satisfactory control performance more accurately than other control methods without control charactering.
Contents
摘要 i
Abstract ii
致謝 iii
Contents iv
List of Table vi
List of Figures vii
Chapter 1 Introduction
1.1 General Remark and Overview of Previous Work 1
1.2 Objectives and Organization of the Thesis 3
Chapter 2 Sliding-Mode Control Systems Design
2.1 Overview 5
2.2 Problem Formulation 6
2.3 Sliding-Mode Control System (SMC) Design 7
2.4 Total Sliding–Mode Control (TSMC) System Design 8
2.5 Adaptive Total Sliding–Mode Control (ATSMC) System Design 11
2.6 Illustrative Examples 13
2.7 Summary 18
Chapter 3 Intelligent Motion Control System Design via Total Sliding-Mode Control Technology
3.1 Overview 47
3.2 Problem Formulation 48
3.3 Intelligent Motion Control System Design 50
3.3.1 Implementation of adaptive FNN controller 51
3.3.2 Adaptive robust controller design 52
3.4 Illustrative Examples 55
3.5 Summary 58
Chapter 4 Conclusions and Suggestions for Future Research
4.1 Conclusions 72
4.2 Suggestions for Future Research 73
Reference 75

List of Tables
Table 4.1 The parameters of biped robot. 74

List of Figures
Fig. 2.1 The block diagram of the SMC feedback control system. 19
Fig. 2.2 The block diagram of the TSMC feedback control system. 19
Fig. 2.3 The block diagram of the TSMC feedback control system. 20
Fig. 2.4 Ship motion control system. 20
Fig. 2.5 Simulation results of the SMC for ship motion system. 21
Fig. 2.5(a) Numerical simulations for ship heading angle. 21
Fig. 2.5(b) Numerical simulations for ship heading angular velocity. 21
Fig. 2.5(c) The sliding surface of ship motion system. 21
Fig. 2.5(d) The associated control effort (rudder angle). 21
Fig. 2.6 Simulation results of the TSMC for ship motion system. 22
Fig. 2.6(a) Numerical simulations for ship heading angle. 22
Fig. 2.6(b) Numerical simulations for ship heading angular velocity. 22
Fig. 2.6(c) The sliding surface of ship motion system. 22
Fig. 2.6(d) The associated control effort (rudder angle). 22
Fig. 2.7 Simulation results with larger uncertainty of the SMC for ship motion system. 23
Fig. 2.7(a) Numerical simulations for ship heading angle. 23
Fig. 2.7(b) Numerical simulations for ship heading angular velocity. 23
Fig. 2.7(c) The sliding surface of ship motion system. 23
Fig. 2.7(d) The associated control effort (rudder angle). 23
Fig. 2.8 Simulation results with larger uncertainty of the TSMC for ship motion system. 24


Fig. 2.8(a) Numerical simulations for ship heading angle. 24
Fig. 2.8(b) Numerical simulations for ship heading angular velocity. 24
Fig. 2.8(c) The sliding surface of ship motion system. 24
Fig. 2.8(d) The associated control effort (rudder angle). 24 63
Fig. 2.9 Simulation results with larger uncertainty of the ATSMC for ship motion system. 25
Fig. 2.9(a) Numerical simulations for ship heading angle. 25
Fig. 2.9(b) Numerical simulations for ship heading angular velocity. 25
Fig. 2.9(c) The sliding surface of ship motion system. 25
Fig. 2.9(d) The associated control effort (rudder angle). 25
Fig. 2.10 Wing rock motion system. 26
Fig. 2.11 Phase-plane trajectories of the uncontrolled wing rock motion system. 26
Fig. 2.12 Simulation results of the SMC for wing rock motion system
(small initial condition). 27
Fig. 2.12(a) Numerical simulations for roll angle. 27
Fig. 2.12(b) Numerical simulations for roll angular velocity. 27
Fig. 2.12(c) The sliding surface of wing rock motion system. 27
Fig. 2.12(d) The associated control effort. 27
Fig. 2.12(e) The associated control effort. 28
Fig. 2.13 Simulation results of the TSMC for wing rock motion system
(small initial condition). 29
Fig. 2.13(a) Numerical simulations for roll angle. 29
Fig. 2.13(b) Numerical simulations for roll angular velocity. 29
Fig. 2.13(c) The total sliding surface of wing rock motion system. 29
Fig. 2.13(d) The associated control effort. 29
Fig. 2.13(e) The phase-plane trajectory. 30
Fig. 2.14 Simulation results with larger uncertainty of the SMC for wing rock motion
system (small initial condition). 31
Fig. 2.14(a) Numerical simulations for roll angle. 31
Fig. 2.14(b) Numerical simulations for roll angular velocity. 31
Fig. 2.14(c) The sliding surface of wing rock motion system. 31
Fig. 2.14(d) The associated control effort. 31
Fig. 2.14(e) The phase-plane trajectory. 32
Fig. 2.15 Simulation results with larger uncertainty of the TSMC for wing rock motion
system (small initial condition). 33
Fig. 2.15(a) Numerical simulations for roll angle. 33
Fig. 2.15(b) Numerical simulations for roll angular velocity. 33
Fig. 2.15(c) The sliding surface of wing rock motion system. 33
Fig. 2.15(d) The associated control effort. 33
Fig. 2.15(e) The phase-plane trajectory. 34
Fig. 2.16 Simulation results with larger uncertainty of the ATSMC for wing rock motion
system (small initial condition). 35
Fig. 2.16(a) Numerical simulations for roll angle. 35
Fig. 2.16(b) Numerical simulations for roll angular velocity. 35
Fig. 2.16(c) The sliding surface of wing rock motion system. 35
Fig. 2.16(d) The associated control effort. 35
Fig. 2.16(e) The phase-plane trajectory. 36
Fig. 2.17 Simulation results with larger uncertainty of the ATSMC for wing rock motion
system (small initial condition). 37
Fig. 2.17(a) Numerical simulations for roll angle. 37
Fig. 2.17(b) Numerical simulations for roll angular velocity. 37
Fig. 2.17(c) The sliding surface of wing rock motion system. 37
Fig. 2.17(d) The associated control effort. 37
Fig. 2.17(e) The phase-plane trajectory. 38
Fig. 2.18 Simulation results with larger uncertainty of the TSMC for wing rock motion
system (small initial condition). 39
Fig. 2.18(a) Numerical simulations for roll angle. 39
Fig. 2.18(b) Numerical simulations for roll angular velocity. 39
Fig. 2.18(c) The total sliding surface of wing rock motion system. 39
Fig. 2.18(d) The associated control effort. 39
Fig. 2.18(e) The phase-plane trajectory. 40
Fig. 2.19 Simulation results with larger uncertainty of the SMC for wing rock motion
system (large initial condition). 41
Fig. 2.19(a) Numerical simulations for roll angle. 41
Fig. 2.19(b) Numerical simulations for roll angular velocity. 41
Fig. 2.19(c) The sliding surface of wing rock motion system. 41
Fig. 2.19(d) The associated control effort. 41
Fig. 2.19(e) The phase-plane trajectory. 42
Fig. 2.20 Simulation results with larger uncertainty of the TSMC for wing rock motion
system (large initial condition). 43
Fig. 2.20(a) Numerical simulations for roll angle. 43
Fig. 2.20(b) Numerical simulations for roll angular velocity. 43
Fig. 2.20(c) The sliding surface of wing rock motion system. 43
Fig. 2.20(d) The associated control effort. 43
Fig. 2.20(e) The phase-plane trajectory. 44
Fig. 2.21 Simulation results with larger uncertainty of the ATSMC for wing rock motion
system (large initial condition). 45
Fig. 2.21(a) Numerical simulations for roll angle. 45
Fig. 2.21(b) Numerical simulations for roll angular velocity. 45
Fig. 2.21(c) The sliding surface of wing rock motion system. 45
Fig. 2.21(d) The associated control effort. 45
Fig. 2.21(e) The phase-plane trajectory. 46
Fig. 3.1 The block diagram of the proposed FNN-based intelligent motion control system. 60
Fig. 3.2 A four-layer fuzzy neural network (FNN). 61 116
Fig. 3.3 Simulation results of the intelligent motion control with I-type adaptive law
for ship motion system. 62
Fig. 3.3(a) Numerical simulations for ship heading angle. 62
Fig. 3.3(b) Numerical simulations for ship heading angular velocity. 62
Fig. 3.3(c) The total sliding surface of ship motion system. 62
Fig. 3.3(d) The associated control effort (rudder angle). 62
Fig. 3.4 Simulation results of the intelligent motion control with PI-type adaptive law
for ship motion system. 63
Fig. 3.4(a) Numerical simulations for ship heading angle. 63
Fig. 3.4(b) Numerical simulations for ship heading angular velocity. 63
Fig. 3.4(c) The total sliding surface of ship motion system. 63
Fig. 3.4(d) The associated control effort (rudder angle). 63
Fig. 3.5 Simulation results of the intelligent motion control with I-type adaptive law
for wing rock motion system (small initial condition). 64
Fig. 3.5(a) Numerical simulations for roll angle. 64
Fig. 3.5(b) Numerical simulations for roll angular velocity. 64
Fig. 3.5(c) The total sliding surface of wing rock motion system. 64
Fig. 3.5(d) The associated control effort. 64
Fig. 3.5(e) The phase-plane trajectory. 65
Fig. 3.6 Simulation results of the intelligent motion control with PI-type adaptive laws
for wing rock motion system (small initial condition). 66
Fig. 3.6(a) Numerical simulations for roll angle. 66
Fig. 3.6(b) Numerical simulations for roll angular velocity. 66
Fig. 3.6(c) The total sliding surface of wing rock motion system. 66
Fig. 3.6(d) The associated control effort. 66
Fig. 3.6(e) The phase-plane trajectory. 67
Fig. 3.7 Simulation results of the intelligent motion control with I-type adaptive laws
for wing rock motion system (large initial condition). 68
Fig. 3.7(a) Numerical simulations for roll angle. 68
Fig. 3.7(b) Numerical simulations for roll angular velocity. 68
Fig. 3.7(c) The total sliding surface of wing rock motion system. 68
Fig. 3.7(d) The associated control effort. 68
Fig. 3.7(e) The phase-plane trajectory. 69
Fig. 3.8 Simulation results of the intelligent motion control with PI-type adaptive laws
for wing rock motion system (large initial condition). 70
Fig. 3.8(a) Numerical simulations for roll angle. 70
Fig. 3.8(b) Numerical simulations for roll angular velocity. 70
Fig. 3.8(c) The total sliding surface of wing rock motion system. 70
Fig. 3.8(d) The associated control effort. 70
Fig. 3.8(e) The phase-plane trajectory. 71
References
[1]J. J. E. Slotine and W. P. Li, Applied Nonlinear Control. Englewood Cliffs, NJ: Prentice-Hall, 1991.
[2]J. Y. Hung, W. Gao, and J. C. Hung, “Variable structure control: a survey,” IEEE Trans. Ind. Electron., vol. 40, no. 1, pp. 2-22, 1993.
[3]J. C. Hung, Total Invariant VSC for Linear and Nonlinear Systems. Seminar given at Harbin Institute of Technology, Harbin, China. 1996.
[4]R. J. Wai, “Adaptive sliding-mode control for induction servomotor drive,” IEE Proc. Electric Power Appl., vol. 147, no. 6, pp. 553-562, 2000.
[5]Z. Liu and C. Li, “Fuzzy neural networks quadratic stabilization output feedback control for biped robots via approach,” IEEE Trans. Syst., Man, Cybern. B, vol. 33, no. 1, pp. 67-84, 2003.
[6]C. F. Hsu, “Self-organizing adaptive fuzzy neural control for a class of nonlinear systems,” IEEE Trans. Neural Networks, vol. 18, no. 4, pp. 1232-1241, 2007.
[7]R. J. Wai and J. D. Lee, “Dynamic control of maglev transportation system via adaptive fuzzy-neural-network,” Proc. of IJCNN, pp. 569-574, 2007.
[8]F. P. Da, “ Fuzzy neural network sliding mode control for long delay time systems based on fuzzy prediction,” Neural Comput. & Appl., vol. 17, no. 5, pp. 531-539, 2008.
[9]J. R. Layne and K. M. Passino, “Fuzzy model reference learning control for cargo ship steering,” IEEE Control Syst. Mag., vol. 13, no. 6, pp. 23-24, 1993.
[10]R. S. Burns, “The use of artificial neural networks for the intelligent optimal control of surface ships,” IEEE J. Oceanic Eng., vol. 20, no. 1, pp. 65-72, 1995.
[11]Y. Yang, “Direct robust adaptive fuzzy control (DRAFC) for uncertain nonlinear systems using small gain theorem,” Fuzzy Sets Syst., vol. 151, no. 1, pp. 79-97, 2005.
[12]G. Rigatos and S. Tzafestas, “Adaptive fuzzy control for ship steering problem,” Mechatronics, vol. 16, no. 8, pp. 479-489, 2006.
[13]P. Konstadinopoulos, D. T. Mook, and A. H. Nayfeh, “Subsonic wing rock of slender delta wings,” J. Aircraft, vol. 22, pp. 223-228, 1985.
[14]C. M. Lin and C. F. Hsu, “Supervisory recurrent fuzzy neural network control of wing rock for slender delta wings,” IEEE Trans. Fuzzy Systems, vol. 12, no. 5, pp. 733-742, 2004.
[15]A. H. Nayfeh, J. M. Elzebda and D.T. Mook, “Analytical study of the subsonic wing-rock phenomenon for slender delta wings”, J. Aircraft, vol. 26, pp. 805–809, 1989.
[16]S.N Singh, W Yim and W.R Wells, “Direct adaptive and neural control of wing-rock motion of slender delta wings”, J. Guid. Control Dyna., vol. 18, pp. 25–30, 1995.
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