ICRA 2011 Paper Abstract


Paper WeP207.2

Arcese, Laurent (University of Orleans), Fruchard, Matthieu (University of Orleans), Beyeler, Felix (ETH Zurich), Ferreira, Antoine (University of Orléans), Nelson, Bradley J. (ETH Zurich)

Adaptive Backstepping and MEMS Force Sensor for an MRI-Guided Microrobot in the Vasculature

Scheduled for presentation during the Regular Sessions "Micro-Nano Robots and Applications to Life Science" (WeP207), Wednesday, May 11, 2011, 15:40−15:55, Room 5B

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on July 14, 2020

Keywords Micro/Nano Robots, Robust/Adaptive Control of Robotic Systems, Medical Robots and Systems


A microrobot consisting of a polymer binded aggregate of ferromagnetic particles is controlled using a Magnetic Resonance Imaging (MRI) device in order to achieve targeted therapy. The primary contribution of this paper is the design of an adaptive backstepping controller coupled with a high gain observer based on a nonlinear model of a microrobot in a blood vessel. This work is motivated by the difficulty in accurately determining many biological parameters, which can result in parametric uncertainties to which model-based approaches are highly sensitive. We show that the most sensitive parameter, magnetization of the microrobot, can be measured using a MEMS force sensor, while the second one, the dielectric constant of blood, can be estimated on line. The efficacy of this approach is illustrated by simulation results.



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