Volume 19, Issue 7 (July 2019)
Modares Mechanical Engineering 2019, 19(7): 1585-1590 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Ebrahimi dehshalie M, Menhaj M, Karrari M. Optimal Control Algorithm Design for the Microfluidic Channel Network Droplet Generation with Output Feedback Delay. Modares Mechanical Engineering 2019; 19 (7) :1585-1590
URL: http://mme.modares.ac.ir/article-15-24692-en.html
URL: http://mme.modares.ac.ir/article-15-24692-en.html
1- Electrical Engineering Faculty, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran
2- Electrical Engineering Faculty, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran ,menhaj@aut.ac.ir
2- Electrical Engineering Faculty, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran ,
Abstract: (8333 Views)
In this paper, the optimal control algorithm design is proposed for droplet generation. In the proposed algorithm, the redundancy in the microfluidic channel network for droplet generation is used to the optimization setting in order to determine volume flow rate of fluid for each input channel; an optimization problem is proposed for minimizing the volume flow rate of fluid such that the droplet formed in the outlet channel is produced at the desired size. Also, due to the importance of estimating the system state, the design of the Luenberger observer (reduced order observer) has been developed. Then, the proposed scheme is robust against output feedback delay with respect to the optimal LQR control structure for tracking the desired value. While designing for the observer and controller sections, the delays in the measurement of the output feedback are considered, and the sustainability analysis for each of the sections has been performed due to the fixed delay in the output feedback. Output feedback is a measurable variable of the input volume flow of each channel. Finally, the optimal control algorithm of droplet generation for a microfluidic structure with a T shape has been stimulated.
Article Type: Original Research |
Subject:
Mechatronics
Received: 2018/09/2 | Accepted: 2018/12/2 | Published: 2019/07/2
Received: 2018/09/2 | Accepted: 2018/12/2 | Published: 2019/07/2
References
1. Sadeghian H, Hojjat Y, Soleimani M. Development of a new method for experimental dielectrophoresis force measurement in the microfluidic cell sorting actuators. Modares Mechanical Engineering. 2017;17(3):150-158. [Persian] [Link]
2. Zhan Y, Wang J, Bao N, Lu C. Electroporation of cells in microfluidic droplets. Analytical Chemistry. 2009;81(5):2027-2031. [Link] [DOI:10.1021/ac9001172]
3. Christopher GF, Anna SL. Microfluidic methods for generating continuous droplet streams. Journal of Physics D Applied Physics. 2007;40(19):R319. [Link] [DOI:10.1088/0022-3727/40/19/R01]
4. Nekouei M, Vanapalli SA. Volume-of-fluid simulations in microfluidic T-junction devices: Influence of viscosity ratio on droplet size. Physics of Fluids. 2017;29(3):032007. [Link] [DOI:10.1063/1.4978801]
5. Zhu P, Wang L. Passive and active droplet generation with microfluidics: A review. Lab on a Chip. 2017;17(1):34-75. [Link] [DOI:10.1039/C6LC01018K]
6. Garstecki P, Fuerstman MJ, Stone HA, Whitesides GM. Formation of droplets and bubbles in a microfluidic T-junction - scaling and mechanism of break-up. Lab on a Chip. 2006;6(3):437-446. [Link] [DOI:10.1039/b510841a]
7. Wong D, Ren CL. Microfluidic droplet trapping, splitting and merging with feedback controls and state space modelling. Lab on a Chip. 2016;16(17):3317-3329. [Link] [DOI:10.1039/C6LC00626D]
8. Miller E, Rotea M, Rothstein JP. Microfluidic device incorporating closed loop feedback control for uniform and tunable production of micro-droplets. Lab on a Chip. 2010;10(10):1293-1301. [Link] [DOI:10.1039/b925497h]
9. Zeng W, Li S, Wang Z. Closed-loop feedback control of droplet formation in a T-junction microdroplet generator. Sensors and Actuators A Physical. 2015;233:542-547. [Link] [DOI:10.1016/j.sna.2015.08.002]
10. Kim YT, Le Duc P, Messner W. Modeling and control of a nonlinear mechanism for high performance microfluidic systems. IEEE Transactions on Control Systems Technology. 2013;21(1):203-211. [Link] [DOI:10.1109/TCST.2011.2172445]
11. Kuczenski B, Le Duc PR, Messner WC. Pressure-driven spatiotemporal control of the laminar flow interface in a microfluidic network. Lab on a Chip. 2007;7(5):647-649. [Link] [DOI:10.1039/b617065j]
12. Derakhshan Sh, Yazdani K. Numerical analysis of a magnetohydrodynamic micropump performance. Modares Mechanical Engineering. 2015;14(13):251-258. [Persian] [Link]
13. Kan J, Tang K, Ren Y, Zhu G, Li P. Study on a piezohydraulic pump for linear actuators. Sensors Actuators A Physical. 2009;149(2):331-339. [Link] [DOI:10.1016/j.sna.2008.12.008]
14. Ebrahimi Dehshalie M, Menhaj MB, Ghasemi A, Karrari M. Finite-time distributed global optimal control for linear time-varying multi-agent systems: A dynamic output-feedback perspective. IET Control Theory & Applications. 2018;12(9):1267-1275. [Link] [DOI:10.1049/iet-cta.2017.0939]
15. Mondie S, Kharitonov VL. Exponential estimates for retarded time-delay systems: An LMI approach. IEEE Transactions on Automatic Control. 2005;50(2):268-273. [Link] [DOI:10.1109/TAC.2004.841916]
16. Anderson BDO, Moore JB. Optimal control: Linear quadratic methods. Chelmsford MA: Courier Corporation; 2007. [Link]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |