Kulcsár C, Pronzato L, Walter E
Laboratoire des Signaux et Systèmes, CNRS-ESE, Plateau de Moulon, Gif-sur-Yvette, France.
Int J Biomed Comput. 1994 Jun;36(1-2):95-101. doi: 10.1016/0020-7101(94)90099-x.
A simple example of intravenous theophylline therapy is used to present and compare various drug administration policies based on stochastic control theory. The simplest approach (Heuristic-Certainty-Equivalence (HCE) control) assumes that the model parameters are known. Prior uncertainty on these parameters can be taken into account by using average optimal (AO) control. The available knowledge about the system can be improved by measuring the drug concentration some time after the beginning of the treatment. This corresponds to the notion of feedback and leads to the HCE feedback (HCEF) and AO feedback (AOF) policies. A further step towards optimality consists in choosing the optimal measurement time given that the final purpose is the control of the system and not the estimation of its parameters. Finally, closed-loop optimal (CLO) control optimally chooses both the dosage regimen and measurement time.
静脉注射茶碱疗法的一个简单例子被用于呈现和比较基于随机控制理论的各种给药策略。最简单的方法（启发式确定性等价（HCE）控制）假定模型参数是已知的。通过使用平均最优（AO）控制，可以考虑这些参数的先验不确定性。在治疗开始后的某个时间测量药物浓度，可以改进关于该系统的现有知识。这与反馈的概念相对应，并导致了HCE反馈（HCEF）和AO反馈（AOF）策略。朝着最优性更进一步的一步在于，鉴于最终目的是控制系统而非估计其参数，选择最优测量时间。最后，闭环最优（CLO）控制最优地选择给药方案和测量时间。
