Adaptive Robust Control of Atmospheric Pressure Plasma Jets in Linear Parameter-Varying Framework

Atmospheric pressure plasma jets (APPJs) hold significant promise in biomedical applications, where safe and efficient operation is critical. In this study, a new data-driven robust control paradigm is proposed for APPJs in the Linear Parameter-varying (LPV) framework. By leveraging Bayesian Neural Networks (BNNs), a state space LPV model is identified to capture the intricate nonlinear dynamics of APPJs while providing statistical insights into the system's behavior. This approach allows for the adaptation of the uncertainty region at each time step, enhancing closed-loop control adaptability, and alleviating the conservativeness of the control design compared to the conventional robust controllers. The proposed robust Model Predictive Control (MPC) design method operates through an online process, where an optimization problem, formulated using Linear Matrix Inequalities (LMIs), computes a time-varying feedback control law. Through extensive simulations, the LPV-based robust control's efficacy in handling modeling discrepancies and external disturbances is assessed. Furthermore, a comparison is made with an alternative control scheme employing MPC with a given LTI model, demonstrating the superior robustness and tracking capabilities of the proposed LPV-MPC-based approach. These findings underscore the potential of the proposed technique to enhance APPJ control across diverse practical scenarios.