Dynamic modeling and motion control of a soft robotic arm segment

Zhi Qiao, Pham H. Nguyen, Panagiotis Polygerinos, Wenlong Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations


Soft robotics has shown great potential in manipulation and human-robot interaction due to its compliant nature. However, soft systems usually have a large degree of freedom and strong nonlinearities, which pose significant challenges for precise modeling and control. In this paper, a linear parameter-varying (LPV) model is developed to describe the dynamics of a soft robotic arm segment. Given the different actuation mechanisms, the LPV models for elongation and bending motions are identified through experimental data. A state-feedback H{infty} controller is designed for the LPV model using a linear matrix inequality (LMI). Simulation of the state-feedback controller indicates that the closed-loop system is stable but with steady-state errors. As a result, an iterative learning control (ILC) with P-type learning function is implemented to improve the tracking performance. Simulation results of the ILC+state-feedback controller show steady-state errors are significantly reduced with iterations. The ILCs+state-feedback controller successfully moves the soft robotic arm segment to its desired position within several iterations in experiments.

Original languageEnglish (US)
Title of host publication2019 American Control Conference, ACC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781538679265
StatePublished - Jul 2019
Event2019 American Control Conference, ACC 2019 - Philadelphia, United States
Duration: Jul 10 2019Jul 12 2019

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Conference2019 American Control Conference, ACC 2019
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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