📝 SummarXiv

Abstract

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 challenges for real-time control. This paper introduces the Neural Parameter-varying Data-enabled Predictive Control (NPV-DeePC) framework to address these issues. By integrating hypernetworks into the neural DeePC paradigm, NPV-DeePC adaptively captures system nonlinearities and parameter variations, dynamically adjusts the neural network's learned representation of the system, enabling accurate multi-step trajectory prediction and control. Simulation studies on surface temperature tracking and thermal dose delivery demonstrate that NPV-DeePC achieves higher accuracy and adaptability than existing controllers. Moreover, its computational efficiency supports real-time implementation, making it a practical approach for precise APPJ control and a generalizable solution for other nonlinear, parameter-varying systems.