标题： 神经参数变化数据使能预测控制冷大气压等离子体射流 显示英文标题

标题： Neural Parameter-varying Data-enabled Predictive Control of Cold Atmospheric Pressure Plasma Jets

摘要： 冷等离子体压力射流（APPJs）在生物医学应用中显示出巨大的潜力，但其固有的复杂性，表现为非线性动力学和对操作条件（如电极到表面的距离）的强敏感性，给实现稳健可靠的实时控制带来了重大挑战。 为解决这些问题，本文提出了神经参数变化数据使能预测控制（NPV-DeePC）框架。 通过将超神经网络（hypernets）集成到神经数据使能预测控制（DeePC）范式中，所提出的方法自适应地捕捉系统非线性和参数变化，相应地更新神经特征空间，并实现高效准确的轨迹预测和控制。 NPV-DeePC框架通过涉及表面温度跟踪和热剂量输送的大量仿真进行了验证。 结果表明，它在准确性和适应性方面优于现有控制器。 NPV-DeePC方法的计算效率使其成为实时应用的可行候选。 这些发现突显了其在推进APPJs安全精确控制方面的潜力，并为其他参数变化的非线性系统提供了可扩展的解决方案。 显示英文摘要

摘要： Cold Atmospheric Pressure Plasma Jets (APPJs) show significant potential for biomedical applications, but their inherent complexity, characterized by nonlinear dynamics and strong sensitivity to operating conditions like tip-to-surface distance, presents considerable challenges for achieving robust and reliable real-time control. To address these issues, this paper presents the Neural Parameter-Varying Data-enabled Predictive Control (NPV-DeePC) framework. By integrating hyper neural networks (hypernets) into the neural Data-enabled Predictive Control (DeePC) paradigm, the proposed method adaptively captures system nonlinearities and parameter variations, updates the neural feature space accordingly, and enables efficient and accurate trajectory prediction and control. The NPV-DeePC framework is validated through extensive simulations involving surface temperature tracking and thermal dose delivery. The results highlight its ability to outperform existing controllers in terms of accuracy and adaptability. The computational efficiency of the NPV-DeePC approach makes it a viable candidate for real-time applications. These findings underscore its potential to advance the safe and precise control of APPJs and provide a scalable solution for other parameter-varying nonlinear systems.