Hostname: page-component-586b7cd67f-g8jcs Total loading time: 0 Render date: 2024-11-27T19:13:35.808Z Has data issue: false hasContentIssue false
  • English
  • Français

Improved RBF network torque control in flexible manipulator actuated by PMAs

Published online by Cambridge University Press:  28 September 2018

Kai Liu ,
Yang Wu ,
Tianming Zhu ,
Yining Chen ,
Yonghua Lu  and
Dongbiao Zhao
Kai Liu*
Affiliation:
Nanjing University of Aeronautics and Astronautics, No. 29, Yu Dao Street Qin Huai District Nanjing City, Nanjing 210016, China. E-mails: [email protected], [email protected], [email protected], [email protected], [email protected]
Yang Wu
Affiliation:
Nanjing University of Aeronautics and Astronautics, No. 29, Yu Dao Street Qin Huai District Nanjing City, Nanjing 210016, China. E-mails: [email protected], [email protected], [email protected], [email protected], [email protected]
Tianming Zhu
Affiliation:
Nanjing University of Aeronautics and Astronautics, No. 29, Yu Dao Street Qin Huai District Nanjing City, Nanjing 210016, China. E-mails: [email protected], [email protected], [email protected], [email protected], [email protected]
Yining Chen
Affiliation:
Nanjing University of Aeronautics and Astronautics, No. 29, Yu Dao Street Qin Huai District Nanjing City, Nanjing 210016, China. E-mails: [email protected], [email protected], [email protected], [email protected], [email protected]
Yonghua Lu
Affiliation:
Nanjing University of Aeronautics and Astronautics, No. 29, Yu Dao Street Qin Huai District Nanjing City, Nanjing 210016, China. E-mails: [email protected], [email protected], [email protected], [email protected], [email protected]
Dongbiao Zhao
Affiliation:
Nanjing University of Aeronautics and Astronautics, No. 29, Yu Dao Street Qin Huai District Nanjing City, Nanjing 210016, China. E-mails: [email protected], [email protected], [email protected], [email protected], [email protected]
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

A Pneumatic Muscle Actuator (PMA) is a new pneumatic component sharing similar characteristics with biological muscles, and the flexible manipulator actuated by PMAs can better reflect the flexibility of the mechanism. First and foremost, based on the study of the characteristics of human shoulder joints, the configuration design of the flexible manipulator is analyzed, and its kinematics and dynamics models are established. Furthermore, with regard to the nonlinearity, time-invariance and uncertainty of the control system, three aspects of improvement are proposed, which are based on the Radial Basis Function (RBF) network torque control algorithm. The Genetic Algorithm is used to optimize the initial values of RBF network parameters; RBF network parameters are adjusted dynamically by using the additional momentum method; the Levenberg--Marquardt (LM) algorithm, instead of the gradient descent method, is adopted to adjust Proportion Integration Differentiation (PID) parameters online in real time. At last, to test the effects that the improved algorithm exerts on the flexible manipulator control system, some physical platform experiments are carried out. It turns out that the control accuracy and robustness of the improved algorithm are well improved, and the mechanism can be controlled better to track the circular arc trajectory. It lays fundamental importance to the practical application for the working environment.

Keywords

Pneumatic muscle actuatorFlexible manipulatorImproved RBF networkTorque control
Type
Articles
Information
Robotica , Volume 37 , Issue 2 , February 2019 , pp. 264 - 280
Copyright
Copyright © Cambridge University Press 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Tondu, B., Ippolito, S. and Guiochet, J., “A seven-degrees-of-freedom robot-arm driven by pneumatic artificial muscles for humanoid robots,” Int. J. Robot. Res. 24 (4), 257–274 (2005).Google Scholar
Godage, I. S., Branson, D. T. and Guglielmino, E., “Pneumatic Muscle Actuated Continuum Arms: Modeling and Experimental Assessment,” Proceedings of the 2012 IEEE International Conference on Robotics and Automation, Minnesota, USA (2012) pp. 4980–4985.Google Scholar
Colbrunn, R. W., Nelson, G. M. and Quinn, R. D., “Design and Control of a Robotic Leg with Braided Pneumatic Actuators,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots & Systems, Cleveland, Ohio (2001) pp. 992–998.Google Scholar
Honda, Y., Miyazaki, F. and Nishikawa, A., “Control of Pneumatic Five-fingered Robot Hand using Antagonistic Muscle Ratio and Antagonistic Muscle Activity,” Proceedings of the 2010 3rd IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, Tokyo, Japan (2010) pp. 337–342.Google Scholar
Jamwal, P. K., Hussain, S. and Ghayesh, M. H., “Impedance control of an intrinsically compliant parallel ankle rehabilitation,” IEEE Trans. Ind. Electron. 63 (6), 3638–3647 (2016).Google Scholar
Tao, G., Xie, J. and Zhou, H., “Research achievements and development trends of pneumatic artificial muscles,” J. Mech. Eng. 45 (10), 75–83 (2009).Google Scholar
Zhao, X., Zi, B. and Lu, Q., “Design, analysis, and control of a cable-driven parallel platform with a pneumatic muscle active support,” Robotica 35 (4), 744–765 (2015).Google Scholar
Liu, K., Ma, T. and Gu, B. T., “A new method to predict force for pneumatic muscle actuators,” Adv. Robot. 29 (17), 1127–1136 (2015).Google Scholar
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, Mexico (2001) pp. 353–358.Google Scholar
Meng, D. Y., Tao, G. L. and Li, A. M., “Adaptive robust control of pneumatic cylinders using fast switching on/off solenoid valves,” J. Mech. Eng. 51 (10), 180–188 (2015).Google Scholar
Yu, H. T., Guo, W. and Tan, H. W., “Design and control on antagonistic bionic joint driven by pneumatic muscles actuators,” J. Mech. Eng. 48 (17), 1–9 (2012).Google Scholar
Shen, W. and Shi, G., “Hybrid position tracking control of a pneumatic artificial muscle,” J. Shanghai Jiaotong Univ. 46 (2), 201–206 (2012).Google Scholar
Lin, L. H., Yen, J. Y. and Wang, F. C., “Robust control for pneumatic muscle actuator system,” Trans. Canad. Soc. Mech. Eng. 37 (3), 581–590 (2013).Google Scholar
Ba, D. X., Dinh, T. Q. and Ahn, K. K., “An integrated intelligent nonlinear control method for a pneumatic artificial muscle,” IEEE-ASME Trans. Mechatron. 21 (4), 1835–1845 (2016).Google Scholar
Wei, Y. F. and Li, X. N., “Design and implementation of a flexible manipulator actuated by pneumatic muscle,” Robot 27 (5), 445–449 (2005).Google Scholar
Liu, Y., Wang, T. and Fan, W., “Mechanism and impedance control of the ball universal joint robot driven by the pneumatic muscle actuator group,” J. Mech. Eng. 49 (15), 28–33 (2013).Google Scholar
Wang, L. H., Jin, Y. Z. and Zhu, H. L., “Design and research of seven degrees of freedom robotic arm driven by pneumatic artificial muscle,” J. Zhejiang Sci.-Tech Univ. 29 (1), 74–78 (2012).Google Scholar
Yang, G., Li, B. R. and Fu, X. Y., “Parallel manipulator platform for pneumatic artificial muscles,” J. Mech. Eng. 42 (7), 39–45 (2006).Google Scholar
Liu, Y., Wang, T. and Fan, W., “Model-free adaptive control for the ball-joint robot driven by PMA group,” Robot. 35 (2), 129–134 (2013).Google Scholar
Hou, Y., Hu, X. and Zeng, D., “Biomimetic shoulder complex based on 3-PSS/S spherical parallel mechanism,” Chin. J. Mech. Eng. 28 (1), 29–37 (2015).Google Scholar
Halder, A. M., Itoi, E. and An, K. N., “Anatomy and biomechanics of the shoulder,” Orthopedic Clin. North Am. 31 (2), 159–176 (2000).Google Scholar
Limb, D., Biomechanics of the Shoulder (Springer. Heidelberg, Berlin, 2014).Google Scholar
Liu, K., Ge, Z. S. and Xu, J. Q., “Kinematic optimization of bionic shoulder driven by pneumatic muscle actuators based on particle swarm optimization,” Trans. Nanjing Univ. Aeronaut. Astronaut. 33 (3), 301–309 (2016).Google Scholar
Liu, K., Xu, J. Q. and Ge, Z. S., “Robust control of 3-DOF parallel robot driven by PMAs based on nominal stiffness model,” Adv. Robot. 31 (10), 531–543 (2017).Google Scholar
Pedre, J. L., O-Molina, L. and Molina-Vilaplana, J., “A modular neural network linking Hyper RBF and AVITE models for reaching moving objects,” Robotica 23 (5), 625–633 (2005).Google Scholar
Liu, J. K., Adaptive Control MATLAB Simulation of RBF Neural Network (Tsinghua University Press, Beijing, China, 2014).Google Scholar
Long, Y., Du, Z. J. and Wang, W. D., “RBF neural network with genetic algorithm optimization based sensitivity amplification control for exoskeleton,” J. Harbin Inst. Technol. 47 (7), 26–30 (2015).Google Scholar
Qi, P., Huang, S. Z. and Wang, W., “Improved RBF network PID algorithm and application in pneumatic servo system,” Chin. Hydraul. Pneumat. 4, 111–117 (2017).Google Scholar