MPC for tracking for anesthesia dynamics

TL;DR

This paper introduces an MPC for tracking approach tailored for anesthesia control, addressing multi-time scale dynamics and non-unique steady states, ensuring stability and feasibility in a simulated patient environment.

Contribution

It presents a novel MPC for tracking formulation that handles multi-time scale anesthesia dynamics and non-unique steady states, with stability guarantees.

Findings

Controller achieves stable anesthesia state tracking in simulations.

Framework ensures recursive feasibility and asymptotic stability.

Effective handling of fast and slow dynamics in anesthesia control.

Abstract

In this paper, an MPC for tracking formulation is proposed for the control of anesthesia dynamics. It seamlessly enables the optimization of the steady-states pair that is not unique due to the MISO nature of the model. Anesthesia dynamics is a multi-time scale system with two types of states characterized, respectively, by fast and slow dynamics. In anesthesia control, the output equation depends only on the fast dynamics. Therefore, the slow states can be treated as disturbances, and compensation terms can be introduced. Subsequently, the system can be reformulated as a nominal one allowing the design of an MPC for tracking strategy. The presented framework ensures recursive feasibility and asymptotic stability, through the design of appropriate terminal ingredients in the MPC for tracking framework. The controller performance is then assessed on a patient in a simulation environment.