ECLIPSE: Adaptable Model Predictive Control on a Chip for Personalized and Point-of-Care Plasma Medicine

University Of California-Berkeley, Berkeley CA

Investigators

Abstract

Biomedical devices are becoming increasingly ubiquitous for medical diagnosis and therapies, wherein point-of-care devices have shown great promise for personalized medicine. Nonetheless, the increasing complexity of medical therapeutics and devices necessitates the development of advanced decision-making and automatic control systems to ensure safe, predictable, and therapeutically effective operation of biomedical devices. To this end, this project focuses on harnessing the medical potential of low-temperature plasma biomedical devices. Some of the most promising plasma medicine applications include treatment of biofilm-related infections, treatment of wounds and skin diseases, assistance in cancer treatment, treatment of virus infections, and surface modification of bioimplants. This project aims to leverage advances in machine learning and optimization-based control to develop intelligent decision-making systems for personalizing plasma dose delivery for individual subjects using point-of-care plasma biomedical devices. Intelligent decision-making systems can create unprecedented opportunities by enabling safe and (therapeutically) effective control of plasma devices for point-of-use plasma medicine and biotechnology applications, for example, in resource-limited communities. The results of this research will be used in a new graduate course on learning-based control, and the PI will partner with the Berkeley Engineers and Mentors program to mentor undergraduate students in conducting high school level science lessons. Despite recent advances in model predictive control (MPC) of plasma biomedical devices, there yet remains important challenges towards personalized and point-of-care plasma biomedical applications. This project aims to develop a systems-theoretic framework for optimal design and data-efficient adaptation of MPC-on-a-chip controllers for safe and personalized plasma treatment of complex interfaces using point-of-care biomedical devices. This objective will be realized through: (1) developing a multi-objective optimization framework for co-design of software and hardware for MPC-on-a-chip for uncertain nonlinear systems with fast dynamics (i.e., kHz sampling rates); (2) developing Bayesian optimization methods for safe and optimal performance-oriented adaptation of MPC-on-a-chip that is especially suited for applications in which performance data for control policy adaptation are scarce; and (3) experimentally demonstrating adaptive MPC-on-a-chip of a cold atmospheric plasma jet with prototypical biomedical applications for bacterial disinfection. The impact of the systems-theoretic developments of this project will be beyond plasmas and can translate to other technical systems in which adaptive MPC-on-a-chip controllers can play a critical role. The project also delivers computational benchmarks and open-source codes to provide a measurable demonstration of the improvements achieved over the state-of-the-art. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.