Self-learning fuzzy control with temporal knowledge for atracurium-inducedneuromuscular block during surgery

Self-learning fuzzy logic control has the important property of accommodati ng uncertain, nonlinear, and time-varying process characteristics. This int elligent control scheme starts with no fuzzy control rules and learns how t o control each process presented to it in real time without the need for de tailed process modeling. In this study we utilize temporal knowledge of gen erated rules to improve control performance. A suitable medical application to investigate this control strategy is atracurium-induced neuromuscular b lock of patients in the operating theater where the patient response exhibi ts high nonlinearity and individual patient dose requirements may vary five fold during an operating procedure. We developed a computer control system utilizing Relaxograph (Datex) measurements to assess the clinical performan ce of a self-learning fuzzy controller in this application. Using a T1 setp oint of 10% of baseline in 10 patients undergoing general surgery we found a mean T1 error of 0.28% (SD = 0.39%) while accommodating a 0.25 to 0.38 mg /kg/h range in the mean atracurium infusion rate. This result compares favo rably with more complex and computationally intensive model-based control s trategies for atracurium infusion. (C) 1999 Academic Press.