Sliding-mode control of a soft robot based on data-driven sparse identification

Dimitrios Papageorgiou*, Guðrún Þóra Sigurðardóttir, Egidio Falotico, Silvia Tolu

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Soft robots are increasingly finding their way into many applications, especially those involving manipulation of sensitive and delicate objects or interaction with humans. However, their high-compliance characteristics pose considerable challenges in obtaining low-complexity yet accurate dynamical models that are suitable for advanced feedback control. This paper proposes a framework for end-effector positioning of a soft robot. First, physics-informed sparse regression is used for deriving a nonlinear mathematical model of the robot dynamics. Then, a control scheme comprising a super-twisting sliding mode controller and a nonlinear input estimator is designed for the positioning of the robot end-effector. Conditions for uniform asymptotic stability of the closed-loop system are given. Finally, experimental tests carried on a real soft robot show the efficacy of the proposed design and its tracking accuracy.

Original languageEnglish
Article number105836
JournalControl Engineering Practice
Number of pages10
Publication statusPublished - 2024


  • Adaptive estimation
  • Data-driven model
  • Nonlinear system identification
  • Sliding mode control
  • Soft robots
  • Sparse regression


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