Padhi Radhakant, Kothari Mangal
Department of Aerospace Engineering, Indian Institute of Science, Bangalore 560012, India.
Comput Methods Programs Biomed. 2007 Sep;87(3):208-24. doi: 10.1016/j.cmpb.2007.05.011. Epub 2007 Jul 6.
Combining the advanced techniques of optimal dynamic inversion and model-following neuro-adaptive control design, an innovative technique is presented to design an automatic drug administration strategy for effective treatment of chronic myelogenous leukemia (CML). A recently developed nonlinear mathematical model for cell dynamics is used to design the controller (medication dosage). First, a nominal controller is designed based on the principle of optimal dynamic inversion. This controller can treat the nominal model patients (patients who can be described by the mathematical model used here with the nominal parameter values) effectively. However, since the system parameters for a realistic model patient can be different from that of the nominal model patients, simulation studies for such patients indicate that the nominal controller is either inefficient or, worse, ineffective; i.e. the trajectory of the number of cancer cells either shows non-satisfactory transient behavior or it grows in an unstable manner. Hence, to make the drug dosage history more realistic and patient-specific, a model-following neuro-adaptive controller is augmented to the nominal controller. In this adaptive approach, a neural network trained online facilitates a new adaptive controller. The training process of the neural network is based on Lyapunov stability theory, which guarantees both stability of the cancer cell dynamics as well as boundedness of the network weights. From simulation studies, this adaptive control design approach is found to be very effective to treat the CML disease for realistic patients. Sufficient generality is retained in the mathematical developments so that the technique can be applied to other similar nonlinear control design problems as well.
结合最优动态逆和模型跟踪神经自适应控制设计的先进技术，提出了一种创新技术来设计用于有效治疗慢性粒细胞白血病（CML）的自动给药策略。使用最近开发的细胞动力学非线性数学模型来设计控制器（药物剂量）。首先，基于最优动态逆原理设计一个标称控制器。该控制器可以有效治疗标称模型患者（可以用此处使用的具有标称参数值的数学模型描述的患者）。然而，由于实际模型患者的系统参数可能与标称模型患者的不同，对此类患者的仿真研究表明，标称控制器要么效率低下，要么更糟，无效；即癌细胞数量的轨迹要么显示出不令人满意的瞬态行为，要么以不稳定的方式增长。因此，为了使药物剂量历史更符合实际且针对特定患者，在标称控制器上增加了一个模型跟踪神经自适应控制器。在这种自适应方法中，在线训练的神经网络促进了一个新的自适应控制器。神经网络的训练过程基于李雅普诺夫稳定性理论，这既保证了癌细胞动力学的稳定性，也保证了网络权重的有界性。从仿真研究中发现，这种自适应控制设计方法对于治疗实际患者的CML疾病非常有效。在数学推导中保留了足够的通用性，以便该技术也可以应用于其他类似的非线性控制设计问题。
