Conference proceeding
Motion Control of Tetrahymena pyriformis Cells with Artificial Magnetotaxis: Model Predictive Control (MPC) Approach
2012 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), pp 2492-2497
01 Jan 2012
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The use of live microbial cells as microscale robots is an attractive premise, primarily because they are easy to produce and to fuel. In this paper, we study the motion control of magnetotactic Tetrahymena pyriformis cells. Magnetotactic T. pyriformis is produced by introducing artificial magnetic dipole into the cells. Subsequently, they can be steered by using an external magnetic field. We observe that the external magnetic field can only be used to affect the swimming direction of the cells, while the swimming velocity depends largely on the cells' own propulsion. Feedback information for control is obtained from a computer vision system that tracks the cell. The contribution of this paper is twofold. First, we construct a discrete-time model for the cell dynamics that is based on first principle. Subsequently, we identify the model parameters using the Least Squares approach. Second, we formulate a model predictive approach for feedback control of magnetotactic T. pyriformis. Both the model fitness and the performance of the feedback controller are verified using experimental data.
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Details
- Title
- Motion Control of Tetrahymena pyriformis Cells with Artificial Magnetotaxis: Model Predictive Control (MPC) Approach
- Creators
- Yan Ou - Rensselaer Polytechnic InstituteDal Hyung Kim - Drexel UniversityPaul Kim - Drexel UniversityMin Jun Kim - Drexel UniversityA. Agung Julius - Rensselaer Polytechnic InstituteIEEEDavid H Kim - Graduate College (2015-)
- Publication Details
- 2012 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), pp 2492-2497
- Series
- IEEE International Conference on Robotics and Automation ICRA
- Publisher
- IEEE
- Number of pages
- 6
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Mechanical Engineering and Mechanics; Office of Graduate Studies
- Web of Science ID
- WOS:000309406702076
- Scopus ID
- 2-s2.0-84864495481
- Other Identifier
- 991019173996404721
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Automation & Control Systems
- Engineering, Electrical & Electronic
- Robotics