Data-driven LPV model predictive control of a cold atmospheric plasma jet for biomaterials processing

Dogan Gidon, Hossam S. Abbas, Angelo D. Bonzanini, David B. Graves, Javad Mohammadpour Velni, Ali Mesbah

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number104725
JournalControl Engineering Practice
Volume109
DOIs
StatePublished - Apr 2021
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

Keywords

  • Atmospheric pressure plasma jets
  • LPV model identification
  • Plasma processing
  • Predictive control

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