TY - JOUR
T1 - Data-driven LPV model predictive control of a cold atmospheric plasma jet for biomaterials processing
AU - Gidon, Dogan
AU - Abbas, Hossam S.
AU - Bonzanini, Angelo D.
AU - Graves, David B.
AU - Mohammadpour Velni, Javad
AU - Mesbah, Ali
N1 - Funding Information:
This work was supported by the US National Science Foundation under Grants 1912757 and 1912772 . The second author H. S. Abbas is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under project 419290163 .
Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2021/4
Y1 - 2021/4
N2 - Cold atmospheric plasmas (CAPs) are increasingly used for treatment of complex surfaces in biomedical and biomaterials processing applications. However, the multivariable, distributed-parameter, and nonlinear nature of CAP dynamics and plasma–surface interactions, coupled with the sensitivity of plasmas to exogenous disturbances, make their safe, reproducible and effective operation challenging. This paper adopts a data-driven linear parameter-varying (LPV) modeling framework to learn a control-oriented model for predictive control of a kHz-excited atmospheric pressure plasma jet in Helium. A hierarchical model-based control strategy is proposed based on a supervisory LPV-based model predictive controller (LPV–MPC) to regulate the nonlinear thermal effects of plasma on a surface. Real-time control experiments demonstrate the effectiveness of the proposed LPV–MPC strategy for the multivariable control of surface temperature and plasma optical intensity, as well as for controlling the spatial delivery of the cumulative thermal effects of the plasma jet on a surface.
AB - Cold atmospheric plasmas (CAPs) are increasingly used for treatment of complex surfaces in biomedical and biomaterials processing applications. However, the multivariable, distributed-parameter, and nonlinear nature of CAP dynamics and plasma–surface interactions, coupled with the sensitivity of plasmas to exogenous disturbances, make their safe, reproducible and effective operation challenging. This paper adopts a data-driven linear parameter-varying (LPV) modeling framework to learn a control-oriented model for predictive control of a kHz-excited atmospheric pressure plasma jet in Helium. A hierarchical model-based control strategy is proposed based on a supervisory LPV-based model predictive controller (LPV–MPC) to regulate the nonlinear thermal effects of plasma on a surface. Real-time control experiments demonstrate the effectiveness of the proposed LPV–MPC strategy for the multivariable control of surface temperature and plasma optical intensity, as well as for controlling the spatial delivery of the cumulative thermal effects of the plasma jet on a surface.
KW - Atmospheric pressure plasma jets
KW - LPV model identification
KW - Plasma processing
KW - Predictive control
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U2 - 10.1016/j.conengprac.2021.104725
DO - 10.1016/j.conengprac.2021.104725
M3 - Article
AN - SCOPUS:85099774837
SN - 0967-0661
VL - 109
JO - Control Engineering Practice
JF - Control Engineering Practice
M1 - 104725
ER -