First-Order Dynamic Modeling and Control of Soft Robots

Thomas George Thuruthel, Federico Renda, Fumiya Iida

Research output: Contribution to journalArticlepeer-review

31 Scopus citations


Modeling of soft robots is typically performed at the static level or at a second-order fully dynamic level. Controllers developed upon these models have several advantages and disadvantages. Static controllers, based on the kinematic relations tend to be the easiest to develop, but by sacrificing accuracy, efficiency and the natural dynamics. Controllers developed using second-order dynamic models tend to be computationally expensive, but allow optimal control. Here we propose that the dynamic model of a soft robot can be reduced to first-order dynamical equation owing to their high damping and low inertial properties, as typically observed in nature, with minimal loss in accuracy. This paper investigates the validity of this assumption and the advantages it provides to the modeling and control of soft robots. Our results demonstrate that this model approximation is a powerful tool for developing closed-loop task-space dynamic controllers for soft robots by simplifying the planning and sensory feedback process with minimal effects on the controller accuracy.

Original languageBritish English
Article number95
JournalFrontiers in Robotics and AI
StatePublished - 21 Jul 2020


  • control
  • dynamic modeling
  • first-order dynamics
  • machine learning
  • model reduction
  • soft robotics


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