Enhanced SynPID by Tracking Varying Targets Applied to General Anaesthesia

Abstract

Closed-loop anaesthesia systems use automated controllers to regulate drug delivery and improve anaesthetic management. Although PID-based strategies have shown promising performance, challenges related to patient variability, robustness, setpoint definition, and clinical implementation remain open. This work presents an enhanced synergistic PID strategy, denoted as ESynPID, for multivariable drug delivery in general anaesthesia. The proposed approach extends the previous SynPID formulation by incorporating a variable setpoint generation mechanism through a PID Inversor, a symmetric weighting strategy for the setpoint feedforward terms, and an updated symmetric control-to-range module. The virtual patient is represented through pharmacokinetic–pharmacodynamic models for propofol, remifentanil, and rocuronium, numerically propagated in a sampled-data framework. The resulting architecture combines tracking-oriented target generation, weighted errors PID control, range correction, open-loop/closed-loop phase switching, and a pump emulation block including safety, quantification, and zero-order hold stages. The proposed controller is evaluated in a cohort of virtual patients under surgical stimulation scenarios and compared with the previous SynPID approach. The results show that ESynPID modifies the perfusion profiles by anticipating drug delivery around stimulus changes and by producing more active tracking behaviour during the maintenance phase. This improves the regulation of the hypnotic monitor, while revealing a trade-off in the nociception response. Overall, the study shows that the proposed tracking and symmetry mechanisms increase the adaptability of the control law, although their effect depends on the monitored variable and on the interaction between the weighting strategy, the inversion mechanism, and the pharmacokinetic dynamics of the patient.

The authors' abstract, as published at the source. Bioengineering, 2026 · DOI ↗