Learning-Based Predictive Linear Parameter-Varying Control of Atmospheric Pressure Plasma Jets¹

Complexity of atmospheric pressure plasma jet dynamics poses a significant challenge for control design, and this letter presents a learning- and scenario-based model predictive control (ScMPC) method in the linear parameter-varying (LPV) framework to tackle this challenge. By leveraging artificial neural networks, an LPV state-space representation of the system dynamics is first learned. The mismatch between this model and real plant is then estimated using Bayesian neural networks, enabling scenario generation for ScMPC design. Soft constraints are imposed in the control design formulation to ensure the feasibility of the underlying optimization problem. Results from extensive simulations are used to compare the proposed framework with a benchmark linear time invariant (LTI)-based ScMPC, demonstrating superior performance in both reference tracking and thermal dose delivery. The proposed approach allows for accurate control of plasma jets while reducing conservatism inherent in either LTI-based approaches or other robust control methods.