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Advanced force control of the 2-axes PAM-based manipulator using adaptive neural networks

Published online by Cambridge University Press:  01 June 2018

Ho Pham Huy Anh,
Cao Van Kien and
Nguyen Thanh Nam
Ho Pham Huy Anh*
Affiliation:
Faculty of Electrical and Electronics Engineering (FEEE), HCM City University of Technology, VNU-HCM, Ho Chi Minh City, Vietnam. E-mail: kiencv@hcmut.edu.vnKey Laboratory of Digital Control and System Engineering (DCSELAB), Ho Chi Minh City University of Technology, VNU-HCM, Ho Chi Minh City, Vietnam. E-mail: thanhnam@dcselab.edu.vn
Cao Van Kien
Affiliation:
Faculty of Electrical and Electronics Engineering (FEEE), HCM City University of Technology, VNU-HCM, Ho Chi Minh City, Vietnam. E-mail: kiencv@hcmut.edu.vn
Nguyen Thanh Nam
Affiliation:
Key Laboratory of Digital Control and System Engineering (DCSELAB), Ho Chi Minh City University of Technology, VNU-HCM, Ho Chi Minh City, Vietnam. E-mail: thanhnam@dcselab.edu.vn
*
*Corresponding author. E-mail: hphanh@hcmut.edu.vn
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Summary

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This paper proposes a detailed investigation on the new neural-based feed-forward PID direct force control (FNN-PID-DF) approach applied to a highly nonlinear 2-axes pneumatic artificial muscle (PAM) manipulator in order to ameliorate its force output performance. Founded on the novel inverse neural NARX model dynamically identified to learn well all nonlinear characteristics of the contact force dynamics of the 2-axes PAM-based manipulator, the novel proposed neural FNN-PID-DF force controller is innovatively implemented in order to directly force control the 2-axes PAM robot system used as a rehabilitation device subjected to internal systematic interactions and external contact force deviations. The performance of the experimental tests has proven the advantages and merits of the new force control method compared to the classical PID force control method. The new neural FNN-PID-DF force controller guides the wrist/hand of subject/patient to successfully generate the predefined desired force values.

Keywords

Adaptive intelligent controlPneumatic artificial muscle2-axes PAM manipulatorAdvanced force controlInverse adaptive neural dynamic modelAdaptive neural FNN-PID-DF direct force control

Information

Type
Articles
Information
Robotica , Volume 36 , Issue 9 , September 2018, pp. 1333 - 1362
Copyright
Copyright © Cambridge University Press 2018 

References

1. Noritsugu, T. and Tanaka, T., “Application of rubber artificial muscle robot arm as a rehabilitation robot,” IEEE/ASME Trans. Mechatron. 2 (4), 259–267 (Dec. 1997).Google Scholar
2. Krebs, H. I., Hogan, N., Aisen, M. L. and Volpe, B. T., “Robot-aided neuro-rehabilitation,” IEEE Trans. Rehab. Eng. 6 (1), 75–87 (Mar. 1998).Google Scholar
3. Ju, M.-S., Lin, C. C. K., Chen, J. R., Cheng, H. S. and Lin, C. W., “Performance of elbow tracking under constant torque disturbance in stroke patients and normal subjects,” Clin. Biomech. 17, 640–649 (2002).Google Scholar
4. Cozens, J. A., “Robotic assistance of an active upper limb exercise in neurological impaired patients,” IEEE Tran. Rehab. Eng. 7 (4), 254–256 (Jun. 1999).Google Scholar
5. Reinkensmeyer, D., Takahashi, C. and Timoszyk, W., “Evaluation of an Assistive Controller for Reaching Following Brain Injury,” Proceedings of the 1st Joint BMES/EMBS Conference (1999), p. 631.Google Scholar
6. Lau, C. Y. and Chai, A., “The development of a low cost pneumatic air muscle actuated anthropomorphic robotic hand,” Procedia Eng. 41, 737–742 (2012).Google Scholar
7. Kobayashi, M., Hirano, J. and Nakamura, T., “Development of Delta-Type Parallel-Link Robot Using Pneumatic Artificial Muscles and MR Clutches for Force Feedback Device,” Proceedings of the International Conference on Intelligent Robotics and Applications (2015) pp. 410–420.Google Scholar
8. Zhao, X., Zi, B. and Qian, L., “Design, analysis, and control of a cable-driven parallel platform with a pneumatic muscle active support,” Robotica 35 (4), 744–765 (2017).Google Scholar
9. Raibert, M. H. and Craig, J. J., “Hybrid position/force control of robot arms,” Trans. ASME J. Dyn. Syst., Meas. Control 102, 126–133 (1981).Google Scholar
10. Liem, D. T., Park, H. G. and Ahn, K. K., “A feedforward NN fuzzy grey predictor-based controller for force control of an electro-hydraulic actuator,” Int. J. Precis. Eng. Manuf. 17 (3), 309–321 (2016).Google Scholar
11. Lum, P. S., Burgar, C. G., Shor, P. C., Majmundar, M. and Van der Loos, H. F. M., “Robot-assisted movement training compared with conventional therapy techniques for the rehabilitation of upper-limb motor function after stroke,” Arch. Phys. Med. Rehab. 83 (7), 952–959 (2002).Google Scholar
12. Ding, L., Xia, K., Gao, H., Liu, G. and Deng, Z., “Robust adaptive control of door opening by a mobile rescue manipulator based on unknown-force-related constraints estimation,” Robotica 36 (1), 119–140 (2018).Google Scholar
13. Caldwell, D. G. and Tsagarakis, G. N., “Development and control of a soft-actuated exoskeleton for use in physiotherapy and training,” J. Auton. Robots 15 (1), 21–33 (2003).Google Scholar
14. Noritsugu, T., Sasaki, D. and Takaiwa, M., “Rehabilitation Robotics: Development of Active Support Splint Driven by Pneumatic Soft Actuator (ASSIST),” Proceedings of the IEEE International Conference on Robotics and Automation, Barcelona, Spain (Apr. 2005).Google Scholar
15. Hall, K. L., Phillips, C. A., Reynolds, D. B., Mohler, S. R., Rogers, D. B. and Neidhard-Doll, A. T., “Haptic control of a pneumatic muscle actuator to provide resistance for simulated isokinetic exercise: Part I–Dynamic test station and human quadriceps dynamic simulator,” Comput. Methods Biomech. Biomed. Eng. 17 (12), 1391–1401 (2014).Google Scholar
16. Egawa, M., Watanabe, T. and Nakamura, T., “Development of a Wearable Haptic Device with Pneumatic Artificial Muscles and MR Brake,” Proceedings of the IEEE Virtual Reality, Arles (2015) pp. 173–174.Google Scholar
17. Hall, K. L., Phillips, C. A., Reynolds, D. B., Mohler, S. R., Rogers, D. B. and Neidhard-Doll, A. T., “Haptic control of a pneumatic muscle actuator to provide resistance for simulated isokinetic exercise; Part II: Control development and testing,” Comput. Methods Biomech. Biomed. Eng. 18 (1), 1–14 (2015).Google Scholar
18. Nagaoka, T., Konishi, Y. and Ishigaki, H., “Nonlinear Optimal Predictive Control of Rubber Artificial Muscle,” Proceedings of the International Society for Optical Engineering, vol. 2595 (Oct. 1995) pp. 54–61.Google Scholar
19. Hamerlain, M., “Anthropomorphic Robot Arm Driven by Artificial Muscles Using a Variable Structure Control,” Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Pittsburgh, PA (1995) pp. 550–555.Google Scholar
20. Karnjanaparichat, T. and Pongvuthithum, R., “Adaptive tracking control of multi-link robots actuated by pneumatic muscles with additive disturbances,” Robotica 35 (11), 2139–2156 (2017).Google Scholar
21. Ganguly, S., Garg, A., Pasricha, A. and Dwivedy, S. K., “Control of pneumatic artificial muscle system through experimental modelling,” Mechatron. 22 (8), 1135–1147 (Dec. 2012).Google Scholar
22. Zhu, X., Tao, G., Yao, B. and Cao, J., “Adaptive robust posture control of a parallel manipulator driven by pneumatic muscles,” Automatica 44, 2248–2257 (2008).Google Scholar
23. Song, C., Xie, S., Zhou, Z. and Hu, Y., “Modeling of pneumatic artificial muscle using a hybrid artificial neural network approach,” Mechatron. 31, 124–131 (2015).Google Scholar
24. Carbonell, P., Jiang, Z. P. and Repperger, D. W., “A Fuzzy Backstepping Controller for a Pneumatic Muscle Actuator System,” Proceedings of the IEEE International Symposium on Intelligent, Control, Mexico City (2001) pp. 353–358.Google Scholar
25. Lilly, J. H. and Chang, X., “Tracking control of a pneumatic muscle by an evolutionary fuzzy controller,” IEEE Intell. Automat. Soft Comput. 9 (3), 227–244 (Sep. 2003).Google Scholar
26. Son, N. N., Anh, H. P. H. and Chau, T. D., “Inverse Kinematics Solution for Robot Manipulator Based on Adaptive MIMO Neural Network Model Optimized by Hybrid Differential Evolution Algorithm,” Proceedings of the International Conference on Robotics and Biomimetics, Bali, Indonesia (Dec. 2014) pp. 2019–2024.Google Scholar
27. Sabzehmeidani, Y., Mailah, M., Hussein, M., Gatavi, E. and Md Zain, M. Z., “A hybrid fuzzy-based robust controller for pneumatically actuated micro robot,” Int. Rev. Modell. Simul. 3, 1308–1316 (2010).Google Scholar
28. Xie, S. Q. and Jamwal, P. K., “An iterative fuzzy controller for pneumatic muscle driven rehabilitation robot,” Expert Syst. Appl. 38 (7), 8128–8137 (2011).Google Scholar
29. Ming-Kun, C., “An adaptive self-organizing fuzzy sliding mode controller for a 2-DOF rehabilitation robot actuated by pneumatic muscle actuators,” Control Eng. Pract. 18 (1), 13–22 (2010).Google Scholar
30. Jahanabadi, H., Mailah, M., Md Zain, M. Z. and Hooi, H. M., “active force with fuzzy logic control of a two-link arm driven by pneumatic artificial muscles,” J. Bionic Eng. 8, 474–484 (2011).Google Scholar
31. Karakasoglu, A., Sudharsanan, S. I. and Sundareshan, M. K., “Identification and decentralized adaptive control using dynamical neural networks with application to robotic robot arms,” IEEE Trans. Neural Netw. 4 (6), 919–930 (Nov. 1993).Google Scholar
32. Katic, D. M. and Vukobratovic, M. K., “Highly efficient robot dynamics learning by decomposed connectionist feed-forward control structure,” IEEE Trans. Syst. Man Cybern. 25 (1), 145–158 (1995).Google Scholar
33. Pham, D. T. and Fahmy, A. A., “Neuro-Fuzzy Modeling and Control of Robot arms for Trajectory Tracking,” Proceedings of the 16th IFAC World Congress, Prague (Jul. 4–8, 2005).Google Scholar
34. Ahn, K. K. and Anh, H. P. H., “System Modeling and Identification of the Two-Link PAM Robot Arm Optimized with Genetic Algorithm,” Proceedings of the IEEE-ICASE International Conference, Busan, South Korea (2006) pp. 356–361.Google Scholar
35. Ahn, K. K. and Anh, H. P. H., “A new approach of modeling and identification of the pneumatic artificial muscle (PAM) robot arm based on recurrent neural network,” Proc. Inst. Mech. Eng., Part I: J. Syst. Control Eng. 221 (8), 1101–1122 (2007).Google Scholar
36. Fan, J., Zhong, J., Zhao, J. and Zhu, Y., “BP neural network tuned PID controller for position tracking of a pneumatic artificial muscle,” Technol. Health Care 23 (Suppl. 2), S231–S238 (2015).Google Scholar
37. Jiang, X., Wang, Z., Zhang, C. and Yang, L., “Fuzzy neural network control of the rehabilitation robotic arm driven by pneumatic muscles,” Ind. Robot An Int. J. 42 (1), 36–43 (2015).Google Scholar
38. Anh, H. P. H., Son, N. N. and Kien, C. V., “Adaptive neural compliant force-position control of serial PAM robot,” J. Intell. Robot. Syst., 89 (3–4), 351–369 (Mar. 2018).Google Scholar
39. Boerlage, M., Steinbuch, M., Lambrechts, P. and van de Wal, M., “Model–Based Feedforward for Motion Systems,” Proceedings of IEEE Conference on Control Applications (2003) pp. 1158–1163.Google Scholar
40. Balasubramanian, K. and Rattan, K. S., “Feed-Forward Control of a Non-Linear Pneumatic Muscle System Using Fuzzy Logic,” Proceedings of IEEE International Conference on Fuzzy Systems, vol. 1 (2003) pp. 272–277.Google Scholar