研究生: |
羅世胤 SHIH-YIN LUO |
---|---|
論文名稱: |
以無跡卡爾曼濾波壓力估測為基礎之單氣壓肌肉驅動單自由度機械手臂適應性積分逆步控制 UKF Pressure Observer based Adaptive Integral Backstepping Control of a Single Pneumatic Muscle Actuated 1-DOF Manipulator |
指導教授: |
姜嘉瑞
Chia-Jui Chiang |
口試委員: |
黃安橋
An-Chyau Huang 江茂雄 MAO-HSIUNG CHIANG |
學位類別: |
碩士 Master |
系所名稱: |
工程學院 - 機械工程系 Department of Mechanical Engineering |
論文出版年: | 2022 |
畢業學年度: | 110 |
語文別: | 中文 |
論文頁數: | 172 |
中文關鍵詞: | 氣壓肌肉致動器 、非線性系統 、時變 、遲滯 、逆步控制 、積分器 、適應性控制 、無跡卡爾曼濾波器 |
外文關鍵詞: | Pneumatic muscle actuator, Nonlinear system, Time variance, Hysteresis, Backstepping control, Integrator, Adaptive control, Unscented kalman filter |
相關次數: | 點閱:490 下載:0 |
分享至: |
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
氣壓肌肉致動器有著優良的功率重量比、成本低、清潔、易於維護、可撓性且安全性佳等優點,使其非常適合用於需要與人體緊密接觸的機器人或醫療輔具中。由於氣壓肌肉屬於複合材料且氣體具可壓縮性,使其具高度非線性、時變及遲滞等特性,形成快速精密運動控制上的挑戰。為了解決上述問題,本論文提出以狀態回授為基礎並藉由物理模型設計之適應性積分逆步控制器,其中PMA內部氣壓可藉由無跡卡爾曼濾波器(UKF)進行估測,以達成單氣壓肌肉驅動之單自由度機械手臂的追跡控制,使其在低成本且輕重量環境下,皆能在不同頻率下維持良好的控制性能。本論文中的單自由度機械手臂,兩側分別採用氣壓肌肉及彈簧,組成不對稱的架構,使得精確的追跡控制更具挑戰性,尤其在高頻追蹤的情況下。首先,在系統模型中加入積分狀態,以提升穩態追跡性能。接著以逆步控制利用李亞普諾夫法則逆向反推,確保由氣壓肌肉驅動之機械手臂及積分項所組成的非線性系統每一層動態之穩定性。接著再加入適應性控制,利用梯度下降法更新參數,在不同操作頻率下最小化追跡誤差。最後再結合無跡卡爾曼濾波器,降低整體設備成本及重量。實驗結果顯示本論文提出的適應性積分逆步控制器,在0.1 Hz到1 Hz的正弦波命令下,皆能一貫地達成精確的追跡控制,達成在1 Hz的正弦波命令下最大追跡誤差約為1.3度,追跡均方根誤差約為0.61度;再結合無跡卡爾曼濾波器後,在估測均方根誤差約為0.64 Bar下,達成最大追跡誤差約為1.5度,追跡均方根誤差約為0.75度。
The advantages of pneumatic muscle actuator (PMA), including high power-to-weight ratio, low cost, cleanness, ease of maintenance, pliability and inherent safety, make it suitable to be utilized in a robot that intimately assists movements of a human body. The complex material composition of the PMAs and compressibility of the air, however, result in high nonlinearity, time variance and hysteresis characteristics of the PMA, posing challenges to fast and precise motion control. To deal with the above mentioned problems, an adaptive integral backstepping controller integrated with unscented kalman filter (UKF) estimating inner pressure of PMAs is developed in this thesis based on state feedback and a physics-based model , to achieve accurate and consistent tracking performance of a single PMA actuated 1-DOF manipulator at various frequencies in low cost and weight situation. The asymmetric structure of the 1-DOF manipulator, with a PMA on one end and a spring on the other, also presents a challenge to precise tracking control especially at higher frequencies. An integral state is first augmented to the system model to improve the steady-state tracking performance. The backstepping controller stabilizes recursively each layer of the dynamics consisting of the nonlinear PMA actuated manipulator and the integrator using the Lyapunov approach. An adaptive algorithm based on gradient descent method is applied to achieve minimum tracking errors at various frequencies. Finally, UKF is integrated with controller to reduce cost and weight of device. Experimental results show that the proposed adaptive integral backstepping controller achieves precise and consistent performance tracking sinusoidal references over frequencies ranging from 0.1Hz to 1Hz. In tracking a 1Hz sinusoidal reference, achieving the maximum tracking error is about 1.3 degrees, and root mean square error (RMSE) of tracking is 0.61 degrees. Integrated with UKF, the maximum tracking error is about 1.5 degrees, and RMSE of tracking is 0.75 degrees under 0.64 Bar of RMSE of estimating.
[1] 王衍凱, 以擴張型卡爾曼濾波器為基礎之感應馬達無感測控制及定子轉子阻抗估
測. 碩士論文, 99 年,台北.
[2] F. Daerden and D. Lefeber, “Pneumatic artificial muscles: actuators for robotics and automation,”
European Journal of Mechanical and Environmental Engineering, vol. 47,
no. 01, pp. 10–21, 2002.
[3] P. Kocis and R. Knoflicek, “Artificial muscles: State of the art and a new technology,”
MM Science Journal, vol. 2017, no. 01, pp. 1668–1673, 2017.
[4] H. A. Baldwin, “Realizable models of muscle function,” Proceeding of the First Rock
Island Arsenal Biomechanics Symposium, New York: Springer, Boston, MA, 1969.
[5] B. Tondu and P. Lopez, “Modeling and control of mckibben artificial muscle robot
actuators,” IEEE Control Systems Magzine, vol. 20, no. 02, pp. 15–38, 2000.
[6] S. Ganguly, A. Garg, A. Pasricha, and S. K. Dwivedy, “Control of pneumatic artificial
muscle system through experimental modelling,” Mechatronics, vol. 22, no. 08,
pp. 1135–1147, 2012.
149
參考文獻
[7] C. Y. Cheng, J. C. Renn, S. Wu, and P. H. Lee, “Development of an artificial flexible arm
using flexible muscle actuators,” Journal of Technology, vol. 33, no. 03, pp. 143–154,
2018.
[8] G. Andrikopoulos, G. Nikolakopoulos, and S. Manesis, “Advanced nonlinear pid-based
antagonistic control for pneumatic muscle actuators,” IEEE Transactions on Industrial
Electronics, vol. 61, no. 12, pp. 6926–6937, 2014.
[9] 鄒貴鉅、李聯旺與徐仰德, “氣壓肌肉仿人機械手臂設計與控制,” 龍華科技大學,
碩士學位論文, 2017.
[10] K. Balasubramanian and K. S. Rattan., “Fuzzy logic control of a pneumatic muscle
system using a linearing control scheme,” 22nd International Conference of the North
American Fuzzy Information Processing Society, pp. 432–436, NAFIPS 2003.
[11] Y. H. Chen, N. Sun, D. K. Liang, Y. D. Qin, and Y. C. Fang, “A neuroadaptive control
method for pneumatic artificial muscle systems with hardware experiments,” MECHANICAL
SYSTEMS AND SIGNAL PROCESSING, vol. 146, NAFIPS 2021.
[12] Y. Cao and J. Huang, “Neural-network-based nonlinear model predictive tracking control
of a pneumatic muscle actuator-driven exoskeleton,” IEEE-CAA JOURNAL OF
AUTOMATICA SINICA, vol. 7, no. 06, pp. 1478–1488, 2020.
[13] T. Hesselroth, K. Sarkar, P. P. V. D. Smagt, and K. Schulten, “Neural network control of
a pneumatic robot arm,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 24,
no. 01, pp. 28–38, 1994.
150
參考文獻
[14] C. J. Chiang and Y. C. Chen, “Neural network fuzzy sliding mode control of pneumatic
muscle actuators,” ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE,
vol. 65, pp. 68–86, 2017.
[15] C. P. Chou and B. Hannaford, “Measurement and modeling of mckibben pneumatic
artificial muscles,” IEEE Transactions on Robotics and Automation, vol. 12, no. 01,
pp. 90–102, 1996.
[16] A. Pujana-Arrese, A. Mendizabal, J. Arenas, R. Prestamero, and J. Landaluze, “Modelling
in modelica and position control of a 1-dof set-up powered by pneumatic muscles,”
Mechatronics, vol. 20, no. 05, pp. 535–552, 2010.
[17] G. Andrikopoulos, G. Nikolakopoulos, and S. Manesis, “Pneumatic artificial muscles:
A switching model predictive control approach,” 10.1016/j.conengprac.2013.09.003,
vol. 21, no. 12, pp. 1653–1664, 2013.
[18] D. W. Repperger, K. R. Johnson, and C. A. Philips, “Nonlinear feedback controller
design of a pneumatic muscle actuator system,” 1999 American Control Conference
(Cat. No. 99CH36251), vol. 3, pp. 1525–1529, 1999.
[19] J. H. Lilly, “Adaptive tracking for pneumatic muscle actuators in bicep and tricep configurations,”
IEEE Transactions on Neural Systems and Rehabilitation Engineering,
vol. 11, no. 03, pp. 333–339, 2003.
[20] Y. Cao, J. Huang, C. H. Xiong, D. Wu, M. Zhang, Z. Li, and Y. Hasegawa, “Adaptive
proxy-based robust control integrated with nonlinear disturbance observer for
pneumatic muscle actuators,” IEEE-ASME TRANSACTIONS ON MECHATRONICS,
vol. 25, no. 04, pp. 1756–1764, 2020.
151
參考文獻
[21] N. Sun, D. K. Liang, Y. M. Wu, Y. H. Chen, Y. D. Qin, and Y. C. Fang, “Adaptive control
for pneumatic artificial muscle systems with parametric uncertainties and unidirectional
input constraints,” IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, vol. 16,
no. 02, pp. 969–979, 2020.
[22] M. H. Chiang, C. W. Chang, Y. N. Chen, and C. P. Chen, “Path tracking control for
single-axial dual pneumatic muscle actuators,” Journal of the Chinese Society of Mechanical
Engineers, vol. 38, no. 06, pp. 579–588, 2017.
[23] L. Zhao, H. Y. Cheng, J. H. Zhang, and Y. Q. Xia, “Adaptive control for a motion
mechanism with pneumatic artificial muscles subject to dead-zones,” MECHANICAL
SYSTEMS AND SIGNAL PROCESSING, vol. 148, no. 01, 2021.
[24] R. E. Kalman, “A new approach to linear filtering and prediction problems,” 1960.
[25] D. Luenberger, “An introduction to observers,” IEEE Transactions on Automatic Control,
vol. 16, no. 6, pp. 596–602, 1971.
[26] R. E. Kalman and R. S. Bucy, “New results in linear filtering and prediction theory,”
1961.
[27] S. J. Julier, J. K. Uhlmann, and H. F. Durrant-Whyte, “A new approach for filtering
nonlinear systems,” vol. 3, pp. 1628–1632, 1995.
[28] T. Kodama and K. Kogiso, “Applications of ukf and enkf to estimation of contraction
ratio of mckibben pneumatic artificial muscles,” pp. 5217–5222, 2017.
[29] K. Yokoyama and K. Kogiso, “Pid position control of mckibben pneumatic artificial
muscle using only pressure feedback,” pp. 3362–3367, 2018.
152
參考文獻
[30] X.-c. Zhu, G.-l. Tao, and J. Cao, “Pressure observer based adaptive robust trajectory
tracking control of a parallel manipulator driven by pneumatic muscles,” Journal of
Zhejiang University-SCIENCE A, vol. 8, no. 12, p. 1928–1937, 2007.
[31] 張智星, “Matlab 在工程上的應用,” 美商麥格羅• 希爾國際股份有限公司台灣分
公司, 2013.
[32] 李宜達, “控制系統設計與模擬,” 全華科技圖書股份有限公司, 2003.
[33] Y. A. Cengel, M. A. Boles, and M. Kanoğlu, Thermodynamics: an engineering approach,
vol. 5. McGraw-hill New York, 2011.
[34] M. Krstic, I. Kanellakopoulos, and P. V. Kokotovic, “Nonlinear and adaptive control
design,” New York:Wiley, 1995.
[35] J.-J. E. Slotine, W. Li, et al., Applied nonlinear control, vol. 199. Prentice hall Englewood
Cliffs, NJ, 1991.
[36] M. S. Grewal and A. P. Andrews, Kalman filtering: Theory and Practice with MATLAB.
John Wiley & Sons, 2014.
[37] 黄小平,王岩, 卡爾曼濾波原理及應用:MATLAB 仿真. 北京:電子工業出版社,
2015.
[38] S. J. Julier and J. K. Uhlmann, “Unscented filtering and nonlinear estimation,” Proceedings
of the IEEE, vol. 92, no. 3, pp. 401–422, 2004.
[39] S. J. Julier, “The spherical simplex unscented transformation,” vol. 3, pp. 2430–2434,
2003.