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Volume 7 Issue 2
Mar.  2020

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2020  >  7(2): 451-460
Ameer Hamza Khan, Zili Shao, Shuai Li, Qixin Wang and Nan Guan, "Which is the Best PID Variant for Pneumatic Soft Robots? An Experimental Study," IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 451-460, Mar. 2020. doi: 10.1109/JAS.2020.1003045
Citation: Ameer Hamza Khan, Zili Shao, Shuai Li, Qixin Wang and Nan Guan, "Which is the Best PID Variant for Pneumatic Soft Robots? An Experimental Study," IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 451-460, Mar. 2020. doi: 10.1109/JAS.2020.1003045
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Which is the Best PID Variant for Pneumatic Soft Robots? An Experimental Study

doi: 10.1109/JAS.2020.1003045
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    Abstract
  • This paper presents an experimental study to compare the performance of model-free control strategies for pneumatic soft robots. Fabricated using soft materials, soft robots have gained much attention in academia and industry during recent years because of their inherent safety in human interaction. However, due to structural flexibility and compliance, mathematical models for these soft robots are nonlinear with an infinite degree of freedom (DOF). Therefore, accurate position (or orientation) control and optimization of their dynamic response remains a challenging task. Most existing soft robots currently employed in industrial and rehabilitation applications use model-free control algorithms such as PID. However, to the best of our knowledge, there has been no systematic study on the comparative performance of model-free control algorithms and their ability to optimize dynamic response, i.e., reduce overshoot and settling time. In this paper, we present comparative performance of several variants of model-free PID-controllers based on extensive experimental results. Additionally, most of the existing work on model-free control in pneumatic soft-robotic literature use manually tuned parameters, which is a time-consuming, labor-intensive task. We present a heuristic-based coordinate descent algorithm to tune the controller parameter automatically. We presented results for both manual tuning and automatic tuning using the Ziegler–Nichols method and proposed algorithm, respectively. We then used experimental results to statistically demonstrate that the presented automatic tuning algorithm results in high accuracy. The experiment results show that for soft robots, the PID-controller essentially reduces to the PI controller. This behavior was observed in both manual and automatic tuning experiments; we also discussed a rationale for removing the derivative term.

     

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    Highlights

    • Comprehensive experimental study on the performance of PID for soft robots.
    • Comparison between manual and automatic PID parameter tuning algorithms.
    • Identifying the peculiarity of PID for soft robots as compared to rigid robots.
    • Discussion on optimal strategy to tune PID parameters.

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