Skip to main content

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

  • Ho Pham Huy Anh (a1) (a2), Cao Van Kien (a1) and Nguyen Thanh Nam (a2)

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.

Corresponding author
*Corresponding author. E-mail:
Hide All
1. Noritsugu, T. and Tanaka, T., “Application of rubber artificial muscle robot arm as a rehabilitation robot,” IEEE/ASME Trans. Mechatron. 2 (4), 259267 (Dec. 1997).
2. Krebs, H. I., Hogan, N., Aisen, M. L. and Volpe, B. T., “Robot-aided neuro-rehabilitation,” IEEE Trans. Rehab. Eng. 6 (1), 7587 (Mar. 1998).
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, 640649 (2002).
4. Cozens, J. A., “Robotic assistance of an active upper limb exercise in neurological impaired patients,” IEEE Tran. Rehab. Eng. 7 (4), 254256 (Jun. 1999).
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.
6. Lau, C. Y. and Chai, A., “The development of a low cost pneumatic air muscle actuated anthropomorphic robotic hand,” Procedia Eng. 41, 737742 (2012).
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.
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), 744765 (2017).
9. Raibert, M. H. and Craig, J. J., “Hybrid position/force control of robot arms,” Trans. ASME J. Dyn. Syst., Meas. Control 102, 126133 (1981).
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), 309321 (2016).
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), 952959 (2002).
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), 119140 (2018).
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), 2133 (2003).
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).
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), 13911401 (2014).
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.
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), 114 (2015).
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.
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.
20. Karnjanaparichat, T. and Pongvuthithum, R., “Adaptive tracking control of multi-link robots actuated by pneumatic muscles with additive disturbances,” Robotica 35 (11), 21392156 (2017).
21. Ganguly, S., Garg, A., Pasricha, A. and Dwivedy, S. K., “Control of pneumatic artificial muscle system through experimental modelling,” Mechatron. 22 (8), 11351147 (Dec. 2012).
22. Zhu, X., Tao, G., Yao, B. and Cao, J., “Adaptive robust posture control of a parallel manipulator driven by pneumatic muscles,” Automatica 44, 22482257 (2008).
23. Song, C., Xie, S., Zhou, Z. and Hu, Y., “Modeling of pneumatic artificial muscle using a hybrid artificial neural network approach,” Mechatron. 31, 124131 (2015).
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.
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), 227244 (Sep. 2003).
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.
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, 13081316 (2010).
28. Xie, S. Q. and Jamwal, P. K., “An iterative fuzzy controller for pneumatic muscle driven rehabilitation robot,” Expert Syst. Appl. 38 (7), 81288137 (2011).
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), 1322 (2010).
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, 474484 (2011).
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), 919930 (Nov. 1993).
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), 145158 (1995).
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).
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.
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), 11011122 (2007).
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), S231S238 (2015).
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), 3643 (2015).
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), 351369 (Mar. 2018).
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.
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.
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

  • ISSN: 0263-5747
  • EISSN: 1469-8668
  • URL: /core/journals/robotica
Please enter your name
Please enter a valid email address
Who would you like to send this to? *



Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed