Electrospinning Sedimentary Microstructure Feedback Control by Tuning Substrate Linear Machine Velocity

Zhang, Haitao; Ren, Gui-Ping; Wu, Yue; Sun, Hongwei; Huang, Jian; Yin, Zhouping; Yuan, Ye

doi:10.1109/tie.2017.2703868

Cited by 7 publications

(2 citation statements)

References 34 publications

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“…A stochastic MPC approach for constrained LPV systems via scenario optimization is presented in [22]. In [23], a robust MPC method is developed on practical electrospinning processes to improve the regularity of the sedimentary bead-on-string pattern suffered by external disturbances and system uncertainties. For the T-S models, the earlier approaches of analyzing stability are based on common quadratic Lyapunov functions (see [24]), which are further improved via slack matrices (see [25]).…”

Section: Introductionmentioning

confidence: 99%

Model predictive control for LPV models with maximal stabilizable model range

Yang

Ding

2019

Asian Journal of Control

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This paper characterizes model predictive control (MPC) for linear parameter varying (LPV) models subject to state and input constraints, which is based on the homogeneous polynomially parameterized (HPP) Lyapunov function and HPP control law with tunable complexity degrees. The controller guarantees the closed‐loop asymptotic stability and finds the control move through the convex optimization. While it is known that this technique can improve the control performance and reduce conservatism, we suggest that it also enlarges, and maximizes with the sufficiently large complexity degrees, the stabilizable LPV model range. The computational burden becomes heavier when the complexity degrees increase. However, the main contributions of this paper are more on theory than on practice. It explores to what extent robust MPC can be applied for stabilization of LPV models. Numerical examples are provided to illustrate the effectiveness of the proposed technique.

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Section: Introductionmentioning

confidence: 99%

Model predictive control for LPV models with maximal stabilizable model range

Yang

Ding

2019

Asian Journal of Control

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“…Process monitoring, e.g., based on multiple process parameters, can permit to also increase throughput by preserving process outcome quality. Recent monitoring systems measure solution pressure 32 , current 33 or even more parameters at the same time 34 and often exploit such measurements inside closed-loop feedback system. Recently the transition region from droplet to jet, i.e.…”

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confidence: 99%

Light-assisted electrospinning monitoring for soft polymeric nanofibers

Lunni

Giordano

Pignatelli

et al. 2020

Sci Rep

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A real-time tool to monitor the electrospinning process is fundamental to improve the reproducibility and quality of the resulting nanofibers. Hereby, a novel optical system integrated through coaxial needle is proposed as monitoring tool for electrospinning process. An optical fiber (OF) is inserted in the inner needle, while the external needle is used to feed the polymeric solution (PEO/water) drawn by the process. The light exiting the OF passes through the solution drop at the needle tip and gets coupled to the electrospun fiber (EF) while travelling towards the nanofibers collector. Numerical and analytical models were developed to assess the feasibility and robustness of the light coupling. Experimental tests demonstrated the influence of the process parameters on the EF waveguide properties, in terms of waveguide length (L), and on the nanofibers diameter distribution, in terms of mean $$\widehat{D}$$ D ^ and normalized standard deviation $$\chi$$ χ . Data analysis reveals good correlation between L and $$\widehat{D}, \chi$$ D ^ , χ (respectively maximum correlation coefficients of $${\rho }_{L,\widehat{D}}$$ ρ L , D ^ = 0.88 and $${\rho }_{L,\chi }$$ ρ L , χ = 0.84), demonstrating the potential for effectively using the proposed light-assisted technology as real-time visual feedback on the process. The developed system can provide an interesting option for monitoring industrial electrospinning systems using multi- or moving needles with impact in the scaling-up of innovative nanofibers for soft systems.

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