Toward Safe Dose Delivery in Plasma Medicine using Projected Neural Network-based Fast Approximate NMPC

Abstract

Atmospheric pressure plasma jets (APPJs) are increasingly used for biomedical applications. Reproducible and effective operation of APPJs hinges on controlling the nonlinear effects of plasma on a target substrate in the face of intrinsic variabilities of the plasma as well as exogenous disturbances. This paper presents a low-memory fast approximate nonlinear model predictive control (NMPC) strategy for an APPJ with prototypical applications in plasma medicine. The NMPC objective is to regulate the delivery of the cumulative thermal effects of plasma to a substrate, while adhering to constraints pertaining to a patient’s safety and comfort. Deep neural networks are used to approximate the implicit NMPC law with a cheap-to-evaluate explicit control law that has low memory requirements. Robust constraint satisfaction is guaranteed by projecting the output of the neural network onto a set that ensures the state stays within an appropriately defined invariant set. Closed-loop simulations and real-time control experiments indicate that the proposed approximate NMPC strategy is effective in handling nonlinear control costs at fast sampling times, while guaranteeing satisfaction of safety-critical system constraints. This work takes a crucial step toward fast NMPC of safety-critical plasma applications using resource-limited embedded control hardware.