Nonlinear composite adaptive control of cancer chemotherapy with online identification of uncertain parameters

摘要

A new composite adaptive control strategy is developed for both of the reduction of cancer tumor volume and the online identification of tumor parameters during the drug delivery process in chemotherapy. This control strategy is developed for three different nonlinear mathematical cell-kill models of the cancer tumor including the log-kill hypothesis, Norton-Simon hypothesis and E. hypothesis. All of these models are considered to have fully parametric uncertainties. The stability, tracking convergence and parameters identification convergence during the chemotherapy process are proved using the Lyapunov method. For the first time, the parameters identification of the uncertain chemotherapy process is investigated for three nonlinear models in addition to the control of tumor volume. The effects of uncertainty amount are studied on the performance of the proposed adaptive controller. Comprehensive results are presented and compared for three cell-kill models. Based on the obtained results, the composite adaptive controller has a robust performance in both of the tumor volume manipulation and the parameters identification in the presence of high uncertainties. It is shown that the identification convergence is achieved even with the existence of 70% uncertainty. Moreover, as the parametric uncertainty increases, very slight variation in the initial tumor volume error (with respect to the desired volume) is occurred. (C) 2018 Elsevier Ltd. All rights reserved.