Collaborative Research: Distributed Predictive Control of Cold Atmospheric Microplasma Jet Arrays for Materials Processing

合作研究：用于材料加工的冷大气微等离子体射流阵列的分布式预测控制

• 批准号: 1912757
• 负责人: Javad Mohammadpour Velni
• 金额: $ 25万
• 依托单位: University of Georgia Research Foundation Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2022-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1912757&HistoricalAwards=false
• 关键词: Collaborative; Research; Distributed; Predictive; Control

Cold atmospheric plasmas are composed of weakly ionized gases containing a mixture of ions, electrons, neutral and excited species, and photons at relatively low temperatures. Cold atmospheric plasmas are emerging as an effective and cheaper alternative to the low-pressure plasma technology and are increasingly used in materials processing and manufacturing applications. In these applications there has been a growing interest in using arrays of microplasma jets to scale up the surface area treated by plasma. This project is focused on addressing challenges involved in the control of such microplasma jets. This research will lead to the development of new models and advanced model-based controllers for plasma jet arrays opening new vistas in other areas such as plasma medicine and biomedical engineering. The outcomes of this work will advance research in data-driven modeling and distributed control of nonlinear distributed-parameter systems with interacting subsystems. The educational and outreach activities planned in this effort are natural extensions of this multidisciplinary research.The overarching goal of this collaborative research between University of California at Berkeley and University of Georgia is to develop a systems theoretic framework for tractable modeling and optimal control of plasma jet arrays for state-of-the-art materials processing applications that critically hinge on uniform treatment of large surface areas. This goal will be realized through fusion of nonlinear systems theory for data-driven modeling of distributed-parameter systems and distributed model predictive control. The key objectives of the project are to: (1) develop a novel framework for non-parametric, input-output modeling of nonlinear distributed-parameter systems; (2) develop a distributed control approach that can systematically coordinate predictive control of spatially distributed but interacting systems; and (3) test the modeling and distributed control methods for effective and reproducible operation of a micro-plasma jet array consisting of several jets through real-time control experiments.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

冷大气等离子体由弱电离气体组成，其中含有离子、电子、中性和激发态物质以及相对较低温度下的光子的混合物。冷大气等离子体作为低压等离子体技术的一种有效且廉价的替代品正在兴起，并越来越多地用于材料加工和制造应用。在这些应用中，使用微等离子体射流阵列来扩大等离子体处理的表面积的兴趣越来越大。该项目的重点是解决涉及控制这种微等离子体射流的挑战。这项研究将导致等离子体射流阵列的新模型和先进的基于模型的控制器的开发，在等离子体医学和生物医学工程等其他领域开辟新的前景。本文的研究成果将推动具有交互子系统的非线性分布参数系统的数据驱动建模和分布控制研究。在这项工作中计划的教育和推广活动是自然延伸的多学科research.The总体目标之间的合作研究的加州大学伯克利分校和格鲁吉亚大学是开发一个系统的理论框架，为国家的最先进的材料加工应用，关键取决于大面积的均匀处理的易处理的建模和等离子体射流阵列的最佳控制。这一目标将通过融合非线性系统理论的数据驱动建模的分布参数系统和分布式模型预测控制来实现。该项目的主要目标是：（1）开发一种新的非参数、非线性分布参数系统的输入输出建模框架;（2）开发一种分布式控制方法，可以系统地协调空间分布但相互作用的系统的预测控制;以及（3）通过真实的实验来测试用于由多个射流组成的微等离子体射流阵列的有效和可再现操作的建模和分布式控制方法。时间控制实验。该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估的支持。

项目成果

Model-free Control Design Using Policy Gradient Reinforcement Learning in LPV Framework

LPV 框架中使用策略梯度强化学习的无模型控制设计

• DOI: 10.23919/ecc54610.2021.9655004
• 发表时间: 2021
• 期刊: 2021 European Control Conference (ECC
• 作者: Bao, Yajie;Velni, Javad Mohammadpour
• 通讯作者: Velni, Javad Mohammadpour

Data-Driven Linear Parameter-Varying Model Identification Using Transfer Learning

• DOI: 10.1109/lcsys.2020.3041407
• 发表时间: 2021-11
• 期刊: IEEE Control Systems Letters
• 影响因子: 3
• 作者: Yajie Bao;Javad Mohammadpour Velni

Learning-based adaptive-scenario-tree model predictive control with probabilistic safety guarantees using Bayesian neural networks

使用贝叶斯神经网络的基于学习的自适应场景树模型预测控制和概率安全保证

• 发表时间: 2022
• 期刊: Proceedings of the American Control Conference
• 作者: Y. Bao, K.J. Chan

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

用于生物材料加工的冷大气等离子体射流的数据驱动 LPV 模型预测控制

• DOI: 10.1016/j.conengprac.2021.104725
• 发表时间: 2021
• 期刊: Control Engineering Practice
• 影响因子: 4.9
• 作者: Gidon, Dogan;Abbas, Hossam S.;Bonzanini, Angelo D.;Graves, David B.;Mohammadpour Velni, Javad;Mesbah, Ali
• 通讯作者: Mesbah, Ali

An Online Transfer Learning Approach for Identification and Predictive Control Design with Application to RCCI Engines

应用于 RCCI 发动机的识别和预测控制设计在线迁移学习方法

• 发表时间: 2020
• 期刊: 2020 ASME Dynamic Systems Control Conference
• 作者: Bao, Yajie;Mohammadpour Velni, Javad;Shahbakhti, Mahdi
• 通讯作者: Shahbakhti, Mahdi