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研究生： 楊嘉祐
Yang, Chia-Yu
論文名稱： 基於前饋補償與肌肉電訊號之比例控制之髖膝下肢動力外骨骼設計與實踐
Design and Implementation of Powered Hip-Knee Lower Limb Exoskeleton based on Feedforward Compensations and EMG Proportional Control
指導教授： 陳建祥
Chen, Jian-Shiang
口試委員: 葉廷仁
Yeh, Ting-Jen
林明璋
Lin, Ming-Chang
學位類別： 碩士
Master
系所名稱： 工學院 - 動力機械工程學系
Department of Power Mechanical Engineering
論文出版年： 2020
畢業學年度： 108
語文別： 中文
論文頁數： 165
中文關鍵詞： 下肢外骨骼 、前饋補償 、肌肉電信號 、摩擦力補償 、比例控制 、抖動控制 、姿態重力補償
外文關鍵詞： Lower Limb Exoskeleton, Feedforward Compensations, EMG, Friction Compensation, Proportional Control, Dither Control, Gravity Compensation
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    • 近年來，感測器的微小化、普及化以及便宜化，促使穿戴式的感測裝置的興起。隨著人口高齡化，還有身體不便以及工業上的需求，使得外骨骼的蓬勃發展。本文延續本實驗室過去所發展的主動式髖膝下肢外骨骼，外骨骼為仿人體下肢的結構，直流馬達與減速機對應到使用者的髖與膝關節。
    相較於實驗室過去的發展，本文在外骨骼系統中發展了兩種模式，第一種為隨動模式，其次為輔助模式。隨動模式讓外骨骼在沒有力感測的情況下配合使用者進行移動，此模式主要採用兩種補償策略，一個是摩擦力補償策略，另一個是外骨骼的姿態重力補償。輔助模式是在隨動模式中加入輔助策略，而輔助策略中有兩個輔助力來源，分別為使用者的肌肉電信號與使用者的姿態。透過上述兩種策略便能讓使用者的動作擺脫預先規劃的動作目標，以及獲得輔助。
    本文對於主要策略中所採用的模型與演算法進行描述，分別有摩擦力補償、姿態重力補償以及肌肉電信號輔助算法。在摩擦力補償算法中採用抖動信號並結合位置回授以降低減速機位置對摩擦力的影響和增加減速機低速端的力矩變化上的感測；在姿態重力補償中，加入使用者下肢與上半身傾角模型以降低腳底正向力估測的誤差；在肌肉電信號輔助中，提出實時對肌肉電信號去偏壓的算法，並在肌肉電信號的比例控制中加入人體下肢模型，以同時對多關節間進行協助。
    在實驗中將所採用的主要策略方法進行實驗並評估。最後，透過將方法實踐於外骨骼系統中，來驗證與說明隨動模式能應用於執行日常動作，以及輔助模式能夠協助使用者。

    Nowadays, the development of the exoskeletons becomes more and more popular due to the rise of technology that makes the sensing systems smaller, cheaper and widespread, and the needs of the disabled, the elders and the industries. Our lab have dig into the field of active Hip-Knee exoskeleton for a plenty of time. This thesis continues the past researches in our lab and improving the system and performance.
    Compare with the former developments, this thesis proposed two modes for the operation of the exoskeleton. One is ‘Follow Mode’, and another is ‘Support Mode’. The main purpose of ‘Follow Mode’ is to let the exoskeleton follow the motion of user without any force sensor. The friction compensation algorithm and the gravity compensation are the main strategies for ‘Follow Mode’. ’Support Mode’ is the mode that exoskeleton is activate in helping the motion of the user. This mode is based on the proportional EMG control and supporting the gravity effect acting on the user. Preprograming motion angles are no longer needed in the operation of the exoskeleton.
    In order to verify and discuss the strategies of ‘Follow Mode’ and ‘Support Mode’, the friction model and compensation algorithm, human model for calibrating the normal force of the feet and the support from the EMG are verified and discussed. At last, the experiments of ‘Follow Mode’ and ‘Support Mode’ are done in order to verify the purpose of the modes.

    致謝 ----------------------------------------------i 摘要 ---------------------------------------------ii Abstract ----------------------------------------iii 第1章 緒論 --------------------------------------1 1.1 研究背景與動機 ----------------------------1 1.2 文獻回顧 ----------------------------------3 1.3 本文架構 ----------------------------------7 第2章 系統描述 ----------------------------------8 2.1 外骨骼系統架構 ----------------------------8 2.1.1 外骨骼機構 ------------------------9 2.1.2 輔助依據 --------------------------9 2.1.3 輔助策略 --------------------------9 2.2 實驗室前人研究成果 -----------------------11 2.2.1 前人研究簡述 ---------------------11 2.2.2 前人策略層 -----------------------12 2.2.3 問題描述 -------------------------13 2.3 研究內容 ---------------------------------14 2.4 上層策略層 -------------------------------14 2.5 本章小結 ---------------------------------17 第3章 策略方法與設計 ---------------------------18 3.1 核心思想與策略架構 -----------------------18 3.2 補償與輔助項設計 -------------------------19 3.2.1 減速機摩擦力補償 -----------------19 3.2.2 姿態重力矩計算 -------------------36 3.2.3 肌肉電信號的輔助 -----------------44 3.2.4 其他項 ---------------------------52 3.3 本章小結 ---------------------------------53 第4章 實驗系統架構 -----------------------------54 4.1 機構 -------------------------------------54 4.2 輔助源 -----------------------------------55 4.2.1 無刷薄型直流馬達 -----------------55 4.2.2 和諧減速機 -----------------------56 4.3 量測系統 ---------------------------------57 4.3.1 穿戴者感知設備 -------------------57 4.3.2 外骨骼量測設備 -------------------62 4.4 電源系統 ---------------------------------64 4.5 控制系統 ---------------------------------66 4.5.1 Maxon EPOS2 型號 375711 ----------66 4.5.2 微處理器組 -----------------------67 4.6 其他系統 ---------------------------------70 4.6.1 集成開發環境 ---------------------70 4.6.2 分析軟件 -------------------------71 4.6.3 電腦人機介面 ---------------------71 4.6.4 手機應用程式 ---------------------72 4.7 實驗設計與架構 ---------------------------73 4.8 本章小結 ---------------------------------74 第5章 實驗結果 ---------------------------------75 5.1 摩擦力 -----------------------------------76 5.1.1 建立模型實驗 ---------------------76 5.1.2 模型測試實驗 ---------------------84 5.1.3 摩擦力補償算法實驗 ---------------91 5.2 姿態重力模型 -----------------------------97 5.2.1 建模與動作 -----------------------97 5.2.2 建模結果 -------------------------97 5.2.3 實驗討論 -------------------------99 5.3 肌肉電信號輔助力矩 ----------------------102 5.3.1 實驗方法 ------------------------102 5.3.2 實驗結果與討論 ------------------102 5.3.3 結果總討論與算法評估 ------------117 5.4 外骨骼- 隨動策略動作實驗 ----------------119 5.4.1 實驗結果 ------------------------119 5.4.2 實驗結果討論與小結 --------------141 5.5 外骨骼- 動作輔助實驗 --------------------142 5.5.1 實驗結果 ------------------------142 5.5.2 實驗結果討論與小結 --------------158 5.6 本章小結 --------------------------------159 第6章 本文貢獻與未來展望 ----------------------160 6.1 總結 ------------------------------------160 6.2 本文貢獻 --------------------------------160 6.3 未來展望 --------------------------------161 參考文獻 ----------------------------------------163

    [1] H. Herr, "Exoskeletons and orthoses: classification, design challenges and future directions," Journal of NeuroEngineering and Rehabilitation, vol. 6, no. 1, p. 21, 2009.
    [2] C. Yang, J. Zhang, Y. Chen, Y. Dong and Y. Zhang, "A Review of exoskeleton-type systems and their key technologies," Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, vol. 222, no. 8, pp. 1599-1612, 2008.
    [3] T. Yan, M. Cempini, C. Oddo and N. Vitiello, "Review of assistive strategies in powered lower-limb orthoses and exoskeletons," Robotics and Autonomous Systems,, vol. 64, pp. 120-136, 2015.
    [4] R. Mosher, "Handyman to Hardiman," SAE Technical Paper, 1967.
    [5] A. Young and D. Ferris, "State of the Art and Future Directions for Lower Limb Robotic Exoskeletons," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 25, no. 2, pp. 171-182, 2017.
    [6] H. Kazerooni and R. Steger, "The Berkeley Lower Extremity Exoskeleton," Journal of Dynamic Systems, Measurement, and Control, vol. 128, no. 1, p. 14, 2006.
    [7] A. Zoss, H. Kazerooni and A. Chu, "Biomechanical design of the Berkeley lower extremity exoskeleton (BLEEX)," IEEE/ASME Transactions on Mechatronics, vol. 11, no. 2, pp. 128-138, 2006.
    [8] C. WALSH, K. ENDO and H. HERR, "A QUASI-PASSIVE LEG EXOSKELETON FOR LOAD-CARRYING AUGMENTATION," International Journal of Humanoid Robotics, vol. 4, no. 3, pp. 487-506, 2007.
    [9] G. Chen, C. Chan, Z. Guo and H. Yu, "A Review of Lower Extremity Assistive Robotic Exoskeletons in Rehabilitation Therapy," Critical Reviews in Biomedical Engineering, vol. 41, no. 4-5, pp. 343-363, 2013.
    [10] A. Esquenazi, M. Talaty, A. Packel and M. Saulino, "The ReWalk Powered Exoskeleton to Restore Ambulatory Function to Individuals with Thoracic-Level Motor-Complete Spinal Cord Injury," American Journal of Physical Medicine & Rehabilitation, vol. 91, no. 11, pp. 911-921, 2012.
    [11] G. Zeilig, H. Weingarden, M. Zwecker, I. Dudkiewicz, A. Bloch and A. Esquenazi, "Safety and tolerance of the ReWalk™exoskeleton suit for ambulation by people with complete spinal cord injury: A pilot study," The Journal of Spinal Cord Medicine, vol. 35, no. 2, pp. 96-101, 2012.
    [12] H. Kawamoto, and Y. Sankai, "Power assist system HAL-3 for gait disorder person," in Computers helping people with special needs, Springer, 2002, pp. 196-203.
    [13] H. Kawamoto and Y. Sankai, "Power assist method based on Phase Sequence and muscle force condition for HAL," Advanced Robotics, vol. 19, no. 7, pp. 717-734, 2005.
    [14] Singh and S. Ram Murat, "Trends and Challenges in EMG Based Control Scheme of Exoskeleton Robots - A Review," [Online]. [Accessed 10 12 2017].
    [15] Z. Khokhar, Z. Xiao and C. Menon, "Surface EMG pattern recognition for real-time control of a wrist exoskeleton," BioMedical Engineering OnLine, vol. 9, no. 1, p. 41, 2010.
    [16] S. Lee, K. Wilson, B. Lock and D. Kamper, "Subject-Specific Myoelectric Pattern Classification of Functional Hand Movements for Stroke Survivors," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 19, no. 5, pp. 558-566, 2011.
    [17] K. Kiguchi, T. Tanaka and T. Fukuda, "Neuro-Fuzzy Control of a Robotic Exoskeleton With EMG Signals," IEEE Transactions on Fuzzy Systems, vol. 12, no. 4, pp. 481-490, 2004.
    [18] S. Au, P. Bonato, and H. Herr, "An EMG-position controlled system for an active ankle-foot prosthesis: An initial experimental study," in Proc. IEEE Int. Conf. on Rehabilitation Robotics, pp. 375-379, 2005.
    [19] C. Fleischer, C. Reinicke and G. Hommel, "Predicting the intended motion with EMG signals for an exoskeleton orthosis controller," 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2005.
    [20] K. Kiguchi and Q. Quan, "Muscle-model-oriented EMG-based control of an upper-limb power-assist exoskeleton with a neuro-fuzzy modifier," 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence), p. 2008.
    [21] Q. Ai, B. Ding, Q. Liu and W. Meng, "A Subject-Specific EMG-Driven Musculoskeletal Model for Applications in Lower-Limb Rehabilitation Robotics," International Journal of Humanoid Robotics, vol. 13, no. 3, p. 1650005, 2016.
    [22] S. Kawai, K. Naruse, H. Yokoi and Y. Kakazu, "A study for control of a power assist device," 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2004.
    [23] K. Ullah and Jung-Hoon Kim, "A mathematical model for mapping EMG signal to joint torque for the human elbow joint using nonlinear regression," 2009 4th International Conference on Autonomous Robots and Agents, 2009.
    [24] P. Cavanagh and P. Komi, "Electromechanical delay in human skeletal muscle under concentric and eccentric contractions," European Journal of Applied Physiology and Occupational Physiology, vol. 42, no. 3, pp. 159-163, 1979.
    [25] 吳苑娟, 肌電訊號的處理、判讀與回授應用, 國立清華大學動力機械工程學系碩士論文, 2010.
    [26] E. Eisenberg and T. Hill, "A cross-bridge model of muscle contraction," Progress in Biophysics and Molecular Biology, vol. 33, pp. 55-82, 1979.
    [27] 李怡臻, 基於肌電訊號之主動式下肢輔具設計, 國立清華大學動力機械工程學系碩士論文, 2015.
    [28] 楊娟, 利用肌電訊號回授控制於穿戴式下肢輔具, 國立清華大學動力機械工程學系碩士論文, 2016.
    [29] 邱日湘, 以肌電訊號回授控制動力下肢輔具及實現上下樓梯與行走, 國立清華大學動力機械工程學系碩士論文, 2017.
    [30] D. Winter, Biomechanics and motor control of human movement, 4th ed Hoboken 編者, N.J, 編者, John Wiley & Sons, 2009, p. 101.
    [31] “https://www.maxongroup.com.tw/EC flat motor(series 244879),” [線上].
    [32] S. Olney and D. Winter, "Predictions of knee and ankle moments of force in walking from EMG and kinematic data," Journal of Biomechanics, vol. 18, no. 1, pp. 9-20, 1985.
    [33] D. Lloyd and T. Besier, "An EMG-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo," Journal of Biomechanics, vol. 36, no. 6, pp. 765-776, 2003.
    [34] M. Sartori, M. Reggiani, D. Farina and D. Lloyd, "EMG-Driven Forward-Dynamic Estimation of Muscle Force and Joint Moment about Multiple Degrees of Freedom in the Human Lower Extremity," PLoS ONE, vol. 7, no. 12, p. e52618, 2012.
    [35] C. Fleischer and G. Hommel, "A Human-Exoskeleton Interface Utilizing Electromyography," IEEE Transactions on Robotics, vol. 24, no. 4, pp. 872-882, 2008.

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