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

TL;DR

This paper presents NPV-DeePC, a neural network-based predictive control method that adaptively manages nonlinear and parameter-sensitive plasma jets, improving accuracy and real-time applicability for biomedical uses.

Contribution

The paper introduces NPV-DeePC, integrating hypernetworks with neural DeePC to adaptively model nonlinear, parameter-varying systems for improved control performance.

Findings

Higher accuracy in temperature tracking and thermal dose delivery.

Enhanced adaptability over existing controllers.

Supports real-time control with computational efficiency.

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.