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Hybrid Dynamic Neural Network and PID Control of Pneumatic Artificial Muscle Using the PSO Algorithm

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Abstract

Pneumatic artificial muscles (PAM) have been recently considered as a prominent challenge regarding pneumatic actuators specifically for rehabilitation and medical applications. Since accomplishing accurate control of the PAM is comparatively complicated due to time-varying behavior, elasticity and ambiguous characteristics, a high performance and efficient control approach should be adopted. Besides of the mentioned challenges, limited course length is another predicament with the PAM control. In this regard, this paper proposes a new hybrid dynamic neural network (DNN) and proportional integral derivative (PID) controller for the position of the PAM. In order to enhance the proficiency of the controller, the problem under study is designed in the form of an optimization trend. Considering the potential of particle swarm optimization, it has been applied to optimally tune the PID-DNN parameters. To verify the performance of the proposed controller, it has been implemented on a real-time system and compared to a conventional sliding mode controller. Simulation and experimental results show the effectiveness of the proposed controller in tracking the reference signals in the entire course of the PAM.

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Authors and Affiliations

  1. School of Mechanical Engineering, Sh. Beheshti University, Tehran, 16589-53571, Iran

    Mahdi Chavoshian, Mostafa Taghizadeh & Mahmood Mazare

Authors
  1. Mahdi Chavoshian
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  2. Mostafa Taghizadeh
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  3. Mahmood Mazare
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Corresponding author

Correspondence to Mostafa Taghizadeh.

Additional information

Mahdi Chavoshian received the B. Sc. degree in mechanical engineering from Shahrood University of Technology, Iran in 2008, and the M. Sc. degree in mechanical engineering from Islamic Azad University South Tehran Branch, Iran in 2011. Currently, he is a Ph. D. degree candidate in mechanical engineering at Department of Mechanical and Energy Engineering, Shahid Beheshti University, Iran.

His research interests include dynamic systems, feedback control systems, specially modeling and control of servo pneumatic systems.

Mostafa Taghizadeh received the B. Sc. and M. Sc. degrees in mechanical engineering from the University of Tehran, Iran in 1995 and 1997, respectively, and the Ph. D. degree in mechanical engineering from K. N. Toosi University of Technology, Iran in 2008. In 2008, he was a faculty member at Power and Water University of Technology, Iran. Currently, he is an assistant professor at Department of Mechanical and Energy Engineering, Shahid Beheshti University, Iran. He has published about 35 refereed journal and conference papers.

His research interests include fluid power control systems, robotics and feedback control systems

Mahmood Mazare received the M. Sc. degree in mechanical engineering from Shahid Beheshti University, Iran in 2016. Currently, he is a member of control and robotics laboratory at Department of Mechanical and Energy Engineering, Shahid Beheshti University, Iran.

His research interests include control, robotics, and dynamic systems.

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Chavoshian, M., Taghizadeh, M. & Mazare, M. Hybrid Dynamic Neural Network and PID Control of Pneumatic Artificial Muscle Using the PSO Algorithm. Int. J. Autom. Comput. 17, 428–438 (2020). https://doi.org/10.1007/s11633-019-1196-5

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  • DOI: https://doi.org/10.1007/s11633-019-1196-5

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