Conference proceeding
Distributed sensing and nonlinear MISO models for predicting the propulsive forces of flexible, multi-DOF robotic fins
2016 IEEE International Conference on Robotics and Automation (ICRA), v 2016-, pp 4729-4736
May 2016
Abstract
Fish are capable of producing a wide repertoire of 3D propulsive forces using their fins, and have inspired the development of compliant, multiple-DOF, robotic fins with similar capabilities. Most of these robotic fins are under open-loop control on propulsive force because the forces are challenging to model. Understanding how to predict propulsive forces for these types of fins would significantly advance the state of the art towards closed-loop control of forces. Distributed sensors within robotic fins have been used to predict propulsive forces using linear models, but these models fail to predict forces when fin kinematics become more complex. The objective of the work presented herein is to understand the use of nonlinear, multiple-input-single-output (MISO) Volterra series models between intrinsic sensory measurements and propulsive forces of a flexible robotic fin. Techniques in nonlinear system identification are used to address model conditioning. Nonlinear models predict the propulsive forces well, capturing features of both thrust and lateral forces. Nonlinear models significantly outperformed linear models both in cost of implementation and performance. The best sensor sampling practice was to sample from multiple locations with both pressure and bending modalities. Distributed sensing paired with nonlinear Volterra series models was successful for predicting the forces created by flexible robotic fins with complex kinematics and multiple degrees of freedom.
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Details
- Title
- Distributed sensing and nonlinear MISO models for predicting the propulsive forces of flexible, multi-DOF robotic fins
- Creators
- Jeff C Kahn - Drexel UniversityJames L Tangorra - Drexel University
- Publication Details
- 2016 IEEE International Conference on Robotics and Automation (ICRA), v 2016-, pp 4729-4736
- Publisher
- IEEE
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Mechanical Engineering and Mechanics
- Web of Science ID
- WOS:000389516204010
- Scopus ID
- 2-s2.0-84977488785
- Other Identifier
- 991019170365804721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Automation & Control Systems
- Robotics