Feedback Regulation of Elastically Decoupled Underactuated Soft Robots

Pustina, P.; Della Santina, C.; De Luca, Alessandro

doi:10.1109/LRA.2022.3150829

Feedback Regulation of Elastically Decoupled Underactuated Soft Robots

Journal article (2022)

Authors

P. Pustina Sapienza University of Rome, Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering

C. Della Santina Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering

Alessandro De Luca Sapienza University of Rome

Research Group

Learning & Autonomous Control (Mechanical, Maritime and Materials Engineering) (TU Delft)

DOI: https://doi.org/10.1109/LRA.2022.3150829

Control Modeling Robots Motion Control Gold Feedback control Soft robotics Nonlinear dynamical systems Symmetric matrices And Learning for Soft Robots Flexible Robotics Mathematical models

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Published Date

2022

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Cognitive Robotics

Research Group

Learning & Autonomous Control

Abstract

The intrinsically underactuated and nonlinear nature of continuum soft robots makes the derivation of provably stable feedback control laws a challenging task. Most of the works so far circumvented the issue either by looking at coarse fully-actuated approximations of the dynamics or by imposing quasi-static assumptions. In this letter, we move a step in the direction of controlling generic soft robots taking explicitly into account their underactuation. A class of soft robots that have no direct elastic couplings between the dynamics of actuated and unactuated coordinates is identified. Considering the actuated variables as output, we prove that the system is minimum phase. We then propose regulators that implement different levels of model compensation. The stability of the associated closed-loop systems is formally proven by Lyapunov/LaSalle techniques, taking into account the nonlinear and underactuated dynamics. Simulation results are reported for two models of 2D and 3D soft robots.

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