Modeling and control of McKibben artificial muscle enhanced with echo state networks

Control Engineering Practice

Volumn 20, Issue 5, 2012, Pages 477-488

  Xing, Kexin ^a   Wang, Yongji ^b   Zhu, Quanmin ^c   Zhou, Hanying ^b  

^a ZHEJIANG UNIVERSITY OF TECHNOLOGY   (China)

^b HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^c UNIVERSITY OF THE WEST OF ENGLAND   (United Kingdom)

Author keywords

Adaptive control; Echo state network; Modeling; Pneumatic muscle (PM); RLS algorithm.

Indexed keywords

ADAPTATION SCHEME; ADAPTIVE CONTROL; CONVENTIONAL TECHNIQUES; DYNAMIC PERFORMANCE; ECHO STATE NETWORKS; EXTERNAL DISTURBANCES; MCKIBBEN ARTIFICIAL MUSCLES; MODELING AND CONTROL; PNEUMATIC ARTIFICIAL MUSCLE; PNEUMATIC MUSCLE (PM); RECURSIVE LEAST SQUARES ALGORITHMS; RLS ALGORITHMS; SYSTEM DYNAMICS;

MATHEMATICAL MODELS; MODELS;

UNCERTAINTY ANALYSIS;

EID: 84862780181     PISSN: 09670661     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.conengprac.2012.01.002     Document Type: Article

References (44)

1. Noritsugu T., Tanaka T. Application of rubber artificial muscle manipulator as a rehabilitation robot. IEEE-ASME Transactions on Mechatronics 1997, 2(4):259-267.
2. Koeneman, E.J., Schultz, R.S., Wolf, S.L., Herring, D.E., & Koeneman, J.B.(2004). A pneumatic muscle hand therapy device. Proceedings of the 26th annual international conference of the IEEE EMBS (pp. 2711-2713). San Francisco.
3. He, J.P., Koeneman, E.J., Schultz, R.S., Herring, D.E., Wanberg, J., & Huang, H. et al.(2005). RUPERT: A device for robotic upper extremity repetitive therapy. Proceedings of the 27th annual international conference of the IEEE EMBS (pp. 6844-6847). Shanghai.
4. Xing, K.X., Xu, Q., He, J.P., Wang, Y.J., Liu, Z.W., & Huang, X.L. (2008). A wearable device for repetitive hand therapy. Proceedings of the 2nd biennial IEEE/RAS-EMBS international conference on biomedical robotics and biomechatronics (pp. 919-923).
5. Tondu B., Lopez P. The McKibben muscle and its use in actuating robot-arms showing similarities with human arm behavior. Industrial Robotics 1997, 24(6):432-439.
6. Caldwell D.G., Tsagarakis N., Medrano-Cerda G.A. Bio-mimetic actuators: polymeric pseudo muscular actuators and pneumatic muscle actuators for biological emulation. Mechatronics 2000, 10(4):499-530.
7. Schulte H.F. The characteristics of the Mckibben artificial muscle. The application of external power in prosthetics and orthotics appendix h, publication 87 1961, National Academy of Sciences, Washington DC, pp. 94-115.
8. Chou C.P., Hannaford B. Measurement and modeling of McKibben pneumatic artificial muscles. IEEE Trans. Rob. and Auto 1996, 12(1):90-102.
9. Klute G.K., Hannaford B. Accounting for elastic energy storage in McKibben artificial muscle actuators. Journal of Dynamics Systems, Measurement, and Control 2000, 122(2):386-388.
10. Colbrunn, R.W., Nelson, G.M. & Quinn, R.D. (2001). Modeling of braiding pneumatic actuators for robotic control. Proceedings of the 2001 IEEE/RSJ international conference on intelligent robots and systems (pp. 1964-1970).
11. Repperger, D.W., Johnson, K.R. & Phillips, C.A. (1999). Nonlinear feedback controller design of a pneumatic muscle actuator system. Proceedings of the American control conference (pp. 1525-1529)..
12. Reynolds D.B., Repperger D.W., Phillips C.A., Bandry G. Dynamic characteristics of pneumatic muscle. Annals of Biomedical Engineering 2003, 31(3):310-317.
13. Vo Minh, T., Tjahjowidodo, T., Ramon H., & Van Brussel, H. (2009a). Non-local memory hysteresis in a pneumatic artificial muscle (PAM). Proceedings of the 17th Mediterranean conference on control and automation (pp. 640-645).
14. Vo Minh T., Tjahjowidodo T., Ramon H., Van Brussel H. A new approach to modeling hysteresis in a pneumatic artificial muscle using the Maxwell-slip model. IEEE/ASME Transaction on Mechatronics 2011, 99:1-10.
15. Vo Minh, T., Kamers, B., Tjahjowidodo, T., Ramon H., & Van Brussel, H. (2010a). Modeling torque-angle hysteresis in a pneumatic muscle manipulator. Proceedings of IEEE/ASME international conference on advanced intelligent mechatronics (pp. 1122-1127).
16. Ahn, K.K. & Anh, H.. P.. H.. (2006). System modeling and identification of the two-link PAM manipulator optimized with genetic algorithm. Proceedings of the IEEE-ICASE international conference (pp. 356-361). Busan,Korea, .
17. Anh H.P.H., Ahn K.K. a new approach of modeling and identification of the pneumatic artificial muscle (PAM) manipulator based on recurrent neural networks. JSCE International Journal of System and Control Engineering: Proceedings of the IMCHE, Part I 2007, 221(I8):1101-1122.
18. Anh H.P.H., Ahn K.K. Dynamic model identification of 2-Axes PAM robot arm using neural MIMO NARX model. Lecture Notes in Computer Science (LNCS) 2009, 5754:473-479.
19. Ahn K.K., Anh H.P.H. Identification of the pneumatic artificial muscle manipulators by MGA-based nonlinear NARX fuzzy model. Mechatronics 2009, 19(2):233-246.
20. Ahn K.K., Anh H.P.H. Hybrid control of a pneumatic artificial muscle (PAM) robot arm using an inverse NARX fuzzy model. Engineering Applications of Artificial Intelligence 2011, 24:697-716.
21. Kawashima, K., Sasaki, T., Ohkubo, A., Miyata, T., & Kagawa, T. (2004). Application of robot arm using fiber knitted type pneumatic artificial rubber muscles. Proceedings of the IEEE international conference on robotics and automation (pp. 4937-4942).
22. Medrano-Cerda, G.A., Bowler, C.J. & Caldwell, D.G. (1995). Adaptive position control of antagonistic pneumatic muscle actuators. Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (pp. 378-383).
23. Djouadi, S.M., Repperger, D.W. & Berlin, J.E. (2001). Gain-scheduling H∞ control of a pneumatic muscle using wireless MEMS sensors. Proceedings of the 44th IEEE's Midwest symposium on circuits and systems (pp.734-737).
24. Carbonell, P., Jiang, Z.P. & Repperger, D.W. (2001). Nonlinear control of a pneumatic muscle actuator: backstepping vs. sliding-mode. Proceedings of the 2001 IEEE international conference on control applications (pp. 167-172).
25. Chang M.K., Liou J.J., Chen M.L. T-S fuzzy model-based tracking control of a one-dimensional manipulator actuated by pneumatic artificial muscles. Control Engineering Practice 2011, 19(12):1442-1449.
26. Shen X.R. Nonlinear model-based control of pneumatic artificial muscle servo systems. Control Engineering Practice 2010, 18(3):311-317.
27. Chang M.K. An adaptive self-organizing fuzzy sliding mode controller for a 2-DOF rehabilitation robot actuated by pneumatic muscle actuators. Control Engineering Practice 2010, 18(1):13-22.
28. Xing K.X., Huang J., Wang Y.J., He J.P., Wu J., Xu Q. Tracking control of pneumatic artificial muscle actuators based on sliding mode and nonlinear disturbance observer. IET Control Theory and Applications 2010, 4(10):2058-2070.
29. Vo Minh, T., Tjahjowidodo, T., Ramon H., & Van Brussel, H. (2009b). Control of a pneumatic artificial muscle (PAM) with model-based hysteresis compensation. Proceedings of IEEE/ASME International conference on advanced intelligent mechatronics (pp. 1082-1087). Singapore.
30. Vo Minh T., Tjahjowidodo T., Ramon H., Van Brussel H. Cascade position control of a single pneumatic artificial muscle mass system with hysteresis compensation. IFAC Mechatronics 2010, 20(3):402-414.
31. Hesselroth T., Sarkar K., Patrick van der Smagt P., Schulten K. Neural network control of a pneumatic robot arm. IEEE Transaction on Systems, Man, and Cybernetics 1994, 24(1):28-38.
32. Anh H.P.H., Ahn K.K. Design and implementation of an adaptive recurrent neural network (ARNN) controller of a pneumatic artificial muscle (PAM) manipulator. IFAC Journal of Mechatronics 2009, 19(6):816-828.
33. Anh, H.. P. H.. (2011b). Implementation of Fuzzy NARX IMC PID control of PAM robot arm using modified genetic algorithms. Proceedings of the 5th IEEE symposium series on computational intelligence (IEEE SSCI-2011), (pp. 10-15). Paris.
34. Tian S.P., Ding G.Q., Yan D.T., et al. Modeling and controlling of artificial muscle system based on neural netwoks. Chinese Journal of Biomedical Engineering 2003, 22(4):300-308.
35. Atiya A.F., Parlos A.G. New results on recurrent network training: unifying the algorithms and accelerating convergence. IEEE Transactions on Neural Network 2000, 11(3):697-709.
36. Pearlmutter B.A. Gradient calculation for dynamic recurrent neural networks: a survey. IEEE Transactions on Neural Networks 1995, 6(5):1212-1228.
37. Jaeger, H. (2001). The echo state approach to analyzing and training recurrent neural networks. GMD Report 148, GMD-German National Research Institute for Computer Science.
38. Maas W., Natschlager T., Markram H. Real-time computing without stable stetes: a new framwork for neural computing based on perturbatio. Neural Computing 2002, 14(11):251-2560.
39. Jaeger, H. (2002a). Short term memory in echo state networks. GMD-Report 152, GMD-German National Research Institute for Computer Science.
40. Plöger, P.G., Arghir, A., Günther, T., et al. (2003). Echo state networks for mobile robot modeling and control. Proceedings of the RoboCup (pp. 157-168).
41. Jaeger H. A tutorial on training recurrent neural networks. GMD-Report 159 2002, German National Research Institute for Computer Science.
42. Ljung L., Soderstrom T. Theory and practice of recursive identification 1983, The MIT Press, pp. 16-21.
43. Tham, M. & Mansoori, S. (1988). Covariance resetting in recursive least squares estimation. Proceedings of the international conference on control (pp. 128-133).
44. Moustakides G.V. Study of the transient phase of the forgetting factor RLS. IEEE Transactions on Signal Processing 1997, 45(10):2468-2476.