Which is the best PID variant for pneumatic soft robots an experimental study

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 research attention in academia as well as in industry during recent years because of their inherent safety in human interaction. However, due to structural flexibility and compliance, the mathematical model of these soft robots is nonlinear with an infinite degree of freedom (DOF). Therefore, the accurate position (or orientation) control and optimization of their dynamic response remains a challenging task. Most of the existing soft robots being used 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 the model-free control algorithms and their ability to optimize the 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 manually tuned, using the Ziegler-Nichols method, and automatic tuning using the proposed algorithm. We used the 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.

A review of soft manipulator research, applications, and opportunities

“…D. Zhang et al (2020) proposed an adaptive stiffness control strategy to improve the robot's physical stability in complex environments. Khan et al (2020) presented comparative performance of several variants of model‐free PID‐controllers based on extensive experimental results. They presented a heuristic‐based coordinate descent algorithm to tune the controller parameter automatically.…”

P-satI-D Shape Regulation of Soft Robots

“…This strategy still relies on model-based cancellations and, as such, it is potentially unrobust to model uncertainties. Remarkably, [14]- [16] investigated experimentally the use of PID regulators for controlling the shape of soft robots. However, these references do not provide stability proof and assume full actuation.…”

Model-Based Nonlinear Feedback Controllers for Pressure Control of Soft Pneumatic Actuators Using On/Off Valves

“…Experimentally tuned PID and on-off controllers have also been extensively used in fluid-powered soft robots ( Khan et al, 2020 ; Xavier et al, 2021b ), such as snake-like ( Onal and Rus, 2013 ), worm-like ( Calderón et al, 2016 ), soft-bodied fish ( Marchese et al, 2014b ), and manta ray ( Suzumori et al, 2007 ) robots. Automatic tuning of ordinary, piecewise, and fuzzy PI controllers using a heuristic-based coordinate descent algorithm is proposed by Khan et al (2020 ), which was shown to generally produce better results than manually tuned parameters. The sliding mode controller in the work of Ibrahim et al (2019 ) outperformed the PID controller in the simulation results; however, the PID controller performed best in experimental work, at the expense of higher overshoot and lower robustness to external forces.…”