FPGA-based explicit model predictive control for closed-loop control of intravenous anesthesia

Over the last decade, anesthesia research community witnessed numerous advances in controllers and their implementation platforms to control the depth of anesthesia (DoA) in a patient undergoing surgery. Today's operating theaters are extremely complex and crowded. New surgical techniques bring new medical technologies and more devices in the operation rooms, which often results in complex configurations, computer based control, and cable clutter. In an effort to reduce hardware size and to the improve quality control of anesthesia, we present a field programmable gate array (FPGA) based explicit model predictive control (EMPC) scheme which can take into account the control and state constraints that naturally arise in anesthesia. Real-time implementation of model predictive control (MPC), mainly requires solving an optimization problem at regular time intervals. We propose an FPGA-based EMPC-on-a-chip algorithm with customized 32-bit floating-point addition, substation, and multiplication algorithms. Simulation results with four compartmental PK-PD model, input constraints and a variable bispectral index (BIS) set-point are presented. The real-time simulation results are achieved with Xilinx's Vertex 4 XC4VLX25-10FF668 FPGA.