This paper presents an experimental study to com-pare the performance of model-free control strategies for pneu-matic 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, mathemat-ical models for these soft robots are nonlinear with an infinite de-gree of freedom (DOF). Therefore, accurate position (or orienta-tion) 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 con-trol algorithms such as PID. However, to the best of our know-ledge, there has been no systematic study on the comparative per-formance of model-free control algorithms and their ability to op-timize 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 experi-mental 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 res-ults for both manual tuning and automatic tuning using the Zieg-ler–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-con-troller 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.