Mason D G, Ross J J, Edwards N D, Linkens D A, Reilly C S
Department of Automatic Control and Systems Engineering, University of Sheffield, South Yorkshire S1 3JD, Sheffield, United Kingdom.
Comput Biomed Res. 1999 Jun;32(3):187-97. doi: 10.1006/cbmr.1999.1507.
Self-learning fuzzy logic control has the important property of accommodating uncertain, nonlinear, and time-varying process characteristics. This intelligent control scheme starts with no fuzzy control rules and learns how to control each process presented to it in real time without the need for detailed process modeling. In this study we utilize temporal knowledge of generated rules to improve control performance. A suitable medical application to investigate this control strategy is atracurium-induced neuromuscular block of patients in the operating theater where the patient response exhibits high nonlinearity and individual patient dose requirements may vary fivefold during an operating procedure. We developed a computer control system utilizing Relaxograph (Datex) measurements to assess the clinical performance of a self-learning fuzzy controller in this application. Using a T1 setpoint 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 favorably with more complex and computationally intensive model-based control strategies for atracurium infusion.
自学习模糊逻辑控制具有适应不确定、非线性和时变过程特性的重要属性。这种智能控制方案在没有模糊控制规则的情况下启动，并学习如何在无需详细过程建模的情况下实时控制呈现给它的每个过程。在本研究中，我们利用生成规则的时间知识来提高控制性能。一个适合研究这种控制策略的医学应用是在手术室中阿曲库铵诱导的患者神经肌肉阻滞，其中患者反应表现出高度非线性，并且在手术过程中个体患者的剂量需求可能相差五倍。我们开发了一种计算机控制系统，利用Relaxograph（Datex）测量来评估自学习模糊控制器在该应用中的临床性能。在10例接受普通外科手术的患者中，使用10%基线的T1设定值，我们发现平均T1误差为0.28%（标准差=0.39%），同时平均阿曲库铵输注速率在0.25至0.38mg/kg/h范围内。该结果与用于阿曲库铵输注的更复杂且计算量大的基于模型的控制策略相比具有优势。
