Adaptive back‐stepping cancer control using Legendre polynomials. Issue 1 (1st February 2020)

• Record Type: Journal Article
• Authors: Khorashadizadeh, Saeed; Akbarzadeh Kalat, Ali
• Is Part Of: IET systems biology. Volume 14:Issue 1(2020)
• Page Start: 8
• Page End: 15
• Publication Date: 2020-02-01
• Subjects: cancer -- closed loop systems -- drugs -- Legendre polynomials -- tumours -- nonlinear control systems -- function approximation -- control nonlinearities -- medical control systems -- patient treatment -- adaptive control -- control system synthesis -- MIMO systems; Legendre polynomials -- model‐free controller -- cancer treatment -- tumour cells -- effector cells -- back‐stepping design procedure -- MIMO cancer immunotherapy -- drug dosage -- nonlinear controller design -- adaptive back‐stepping cancer control -- closed‐loop system -- function approximation; Systems biology -- Periodicals; Cell physiology -- Periodicals; Biological systems -- Mathematical models -- Periodicals; Genetics -- Mathematical models -- Periodicals; Computational biology -- Periodicals; 573
• DOI: 10.1049/iet-syb.2019.0038
• Languages: English
• ISSNs: 1751-8849

Abstract

Abstract : Here, a model‐free controller for cancer treatment is presented. The treatment objective is to find a proper drug dosage that can reduce the population of tumour cells. Recently, some solutions have been proposed according to the control theory. In these approaches, based on the mathematical description of the number of effector cells, tumour cells, and concentration of the interleukin‐2 (IL‐2), a non‐linear controller is designed. Here, based on the back‐stepping design procedure and function approximation property of Legendre polynomials, a novel controller for MIMO cancer immunotherapy is presented. In fact, Legendre polynomials play the role of uncertainty estimation and compensation. In comparison with other uncertainty estimators such as neural networks, Legendre polynomials have simpler structure. Thus, the contribution of this study is simplifying the design procedure and reducing the controller computational load in comparison with Neuro‐Fuzzy controllers. The resulting closed‐loop system is capable of overcoming various uncertainties. Simulation results verify the efficiency of the proposed method in the fast reduction of tumour cells. Moreover, a comparison between the performance of Legendre polynomials and a radial basis functions neural network (RBFN) is presented.