Hostname: page-component-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2024-12-23T06:16:20.880Z Has data issue: false hasContentIssue false
  • English
  • Français

Improved fuzzy sliding mode control in flexible manipulator actuated by PMAs

Published online by Cambridge University Press:  22 April 2022

Fang Li ,
Zheng Zhang ,
Yang Wu ,
Yining Chen ,
Kai Liu Open the ORCID record for Kai Liu [Opens in a new window]  and
Jiafeng Yao
Fang Li
Affiliation:
Sanjiang University, No.10, Longxin Road, Yuhuatai District, Nanjing City, Nanjing 210016, China
Zheng Zhang
Affiliation:
Nanjing University of Aeronautics and Astronautics, No. 29, Yu Dao Street, Qin Huai District, Nanjing City, Nanjing 210016, China
Yang Wu
Affiliation:
Nanjing University of Aeronautics and Astronautics, No. 29, Yu Dao Street, Qin Huai District, Nanjing City, Nanjing 210016, China
Yining Chen
Affiliation:
Nanjing University of Aeronautics and Astronautics, No. 29, Yu Dao Street, Qin Huai District, Nanjing City, Nanjing 210016, China
Kai Liu*
Affiliation:
Nanjing University of Aeronautics and Astronautics, No. 29, Yu Dao Street, Qin Huai District, Nanjing City, Nanjing 210016, China
Jiafeng Yao
Affiliation:
Nanjing University of Aeronautics and Astronautics, No. 29, Yu Dao Street, Qin Huai District, Nanjing City, Nanjing 210016, China
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Pneumatic muscle actuator (PMA) similar to biological muscle is a new type of pneumatic actuator. The flexible manipulator based on PMAs was constructed to simulate the actual movement of the human upper arm. Considering the model errors and external disturbances, the fuzzing sliding mode control based on the saturation function was proposed. Compared with other fuzzy control methods, fuzzy control and saturation function are used to adjust the robust terms to improve the tracking accuracy and reduce the high-frequency chattering.

Keywords

pneumatic muscle actuatorsliding mode controlfuzzy controlPI robust sliding mode controlprecise trajectory tracking control
Type
Research Article
Information
Robotica , Volume 40 , Issue 8 , August 2022 , pp. 2683 - 2696
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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

Zhu, L., Shi, X., Chen, Z., Zhang, H.-T. and Xiong, C.-H., “Adaptive servomechanism of pneumatic muscle actuators with uncertainties,” IEEE Trans. Ind. Electron. 64(4), 3329–3337 (2017).CrossRefGoogle Scholar
Cullell, A., Moreno, J. C., Rocon, E., Forner-Cordero, A. and Pons, J. L., “Biologically based design of an actuator system for a knee–ankle–foot orthosis,” Mech. Mach. Theory 44(4), 860–872 (2009).CrossRefGoogle Scholar
Hošovský, A., Piteľ, J., Židek, K., Tóthová, M., Sárosi, J. and Cveticanin, L., “Dynamic characterization and simulation of two-link soft robot arm with pneumatic muscles,” Mech. Mach. Theory 103, 98–116 (2016). http://dx.doi.org/10.1016/j.mechmachtheory.2016.04.013 CrossRefGoogle Scholar
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 (2015).CrossRefGoogle Scholar
Honda, Y., Miyazaki, F. and Nishikawa, A., “Control of Pneumatic Five-Fingered Robot Hand Using Antagonistic Muscle Ratio and Antagonistic Muscle Activity,” 2010 3rd IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob) (2010).CrossRefGoogle Scholar
Liu, K., Wu, Y., Zhu, T., Chen, Y., Lu, Y. and Zhao, D., “Improved RBF network torque control in flexible manipulator actuated by PMAs,” Robotica 37(2), 264–280 (2018).CrossRefGoogle Scholar
Zhu, X., Tao, G., Yao, B. and Cao, J., “Adaptive robust posture control of a parallel manipulator driven by pneumatic muscles,” Automatica 44(9), 2248–2257 (2008).CrossRefGoogle Scholar
Ba, D. X. and Ahn, K. K., “Indirect sliding mode control based on gray-box identification method for pneumatic artificial muscle,” Mechatronics 32, 1–11 (2015). https://doi.org/10.1016/j.mechatronics.2015.09.005 CrossRefGoogle Scholar
Ahn, K. K. and Anh, H. P. H., “Design and implementation of an adaptive recurrent neural networks (ARNN) controller of the pneumatic artificial muscle (PAM) manipulator,” Mechatronics 19(6), 816–828 (2009).CrossRefGoogle Scholar
Amato, F., Colacino, D., Cosentino, C. and Merola, A., “Robust and Optimal Tracking Control for Manipulator Arm Driven by Pneumatic Muscle Actuators,” 2013 IEEE International Conference on Mechatronics (ICM) (2013) pp. 827–834.Google Scholar
Xie, S., Mei, J., Liu, H. and Wang, Y., “Hysteresis modeling and trajectory tracking control of the pneumatic muscle actuator using modified Prandtl–Ishlinskii model,” Mech. Mach. Theory 120, 213–224 (2018). https://doi.org/10.1177/1729881418773204 CrossRefGoogle Scholar
Zhang, J., Merced, E., Sepúlveda, N. and Tan, X., “Optimal compression of generalized Prandtl–Ishlinskii hysteresis models,” Automatica 57, 170–179 (2015). https://doi.org/10.1016/j.automatica.2015.04.012 CrossRefGoogle Scholar
Gu, B., Liu, K. and Ma, T., “Application of BP neural networks in force prediction of pneumatic muscle actuators,” China Meas. Testing Technol. 41(12), 115–118 (2015).Google Scholar
Anh, H. P. H., Kien, C. V., Son, N. N. and Nam, N. T., “New approach of sliding mode control for nonlinear uncertain pneumatic artificial muscle manipulator enhanced with adaptive fuzzy estimator,” Int. J. Adv. Rob. Syst. 15(3), 172988141877320 (2018). CrossRefGoogle Scholar