Decentralized Trajectory Tracking Control for Soft Robots Interacting with the Environment

Franco Angelini, Cosimo Della Santina, Manolo Garabini, Matteo Bianchi, Gian Maria Gasparri, Giorgio Grioli, Manuel Giuseppe Catalano, Antonio Bicchi

Research output: Contribution to journalArticlepeer-review

47 Scopus citations


Despite the classic nature of the problem, trajectory tracking for soft robots, i.e., robots with compliant elements deliberately introduced in their design, still presents several challenges. One of these is to design controllers which can obtain sufficiently high performance while preserving the physical characteristics intrinsic to soft robots. Indeed, classic control schemes using high-gain feedback actions fundamentally alter the natural compliance of soft robots effectively stiffening them, thus de facto defeating their main design purpose. As an alternative approach, we consider here using a low-gain feedback, while exploiting feedforward components. In order to cope with the complexity and uncertainty of the dynamics, we adopt a decentralized, iteratively learned feedforward action, combined with a locally optimal feedback control. The relative authority of the feedback and feedforward control actions adapts with the degree of uncertainty of the learned component. The effectiveness of the method is experimentally verified on several robotic structures and working conditions, including unexpected interactions with the environment, where preservation of softness is critical for safety and robustness.

Original languageEnglish
Article number8370084
Pages (from-to)924-935
Number of pages12
JournalIEEE Transactions on Robotics
Issue number4
StatePublished - Aug 2018
Externally publishedYes


  • Articulated soft robots
  • iterative learning control (ILC)
  • motion control


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