Optimal adaptive intuitionistic fuzzy logic control of anticancer drug delivery systems Abstract   This paper introduces a new closed loop fuzzy logic controller for regulating intravenous anticancer drug delivery, based on intuitionistic fuzzy sets and invasive weed optimization (IWO) algorithm. The developed intuitionistic fuzzy logic controller (IFLC) contributes the following advancements: First, the parameters of IFLC are adaptive and optimally tuned to achieve desired drug concentrations at the tumor site, killing almost all cancer cells at the end of treatment time. Second, drug delivery constraints; namely allowed levels of the drug dose and cumulative toxicity are naturally implied in the design of the optimized IFLC to ensure the clinical safety of cancer patients. Finally, the developed drug delivery control system is robust to handle all possible physiological conditions during treatment such as patient sensitivity in response to toxicity. To test and validate the performance of developed IFLC, extensive simulation results and comparative evaluation have been done on a mathematical patient model. It is proved that our optimal adaptive IFLC is superior to other methods in previous related studies, resulting in the best performance index and the minimum number of remaining cancer cells of 27.63 and 0.806, respectively.

A dual approach for positive Txe2x80x93S fuzzy controller design and its application to cancer treatment under immunotherapy and chemotherapy Abstract   This study proposes an effective positive control design strategy for cancer treatment by resorting to the combination of immunotherapy and chemotherapy. The treatment objective is to transfer the initial number of tumor cells and immunexe2x80x93competent cells from the malignant region into the region of benign growth where the immune system can inhibit tumor growth. In order to achieve this goal, a new modeling strategy is used that is based on Takagixe2x80x93Sugeno. A TakagiSugeno fuzzy model is derived based on the Stepanova nonlinear model that enables a systematic design of the controller. Then, a positive Parallel Distributed Compensation controller is proposed based on a linear copositive Lyapunov Function so that the tumor volume and administration of the chemotherapeutic and immunotherapeutic drugs is reduced, while the density of the immunecompetent cells is reached to an acceptable level. Thanks to the proposed strategy, the entire control design is formulated as a Linear Programming problem. Finally, the simulation results show the effectiveness of the proposed control approach for the cancer treatment.