Personalized drug administration for cancer treatment using Model Reference Adaptive Control

Proceedings of the 2015 16th International Carpathian Control Conference (ICCC)

2015

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This paper presents a model reference adaptive control design approach for a class of nonlinear systems. A nonlinear reference model is considered in the design of adaptive control for the nonlinear system. The design approach is based on successive linear time varying (LTV) approximations of the nonlinear reference model and the system. The convergence proof of the responses of successive approximations to the response of nonlinear system is given. Provided that there exists an adaptive control for the approximated system, the model reference adaptive control is then designed for LTV systems whose responses converge to the response of the nonlinear system. The proposed model reference adaptive control approach is illustrated with an example which shows the effectiveness of the method.

Section: Mechanical Engineering Departmentmentioning

confidence: 99%

Model reference adaptive control design for nonlinear systems using linear time varying approximations

Kodalak

Proceedings of the 2015 16th International Carpathian Control Conference (ICCC)

2015

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“…It was used for addressing the systematic and straightforward methodology to obtain a solution (stabilizing control signal) for nonlinear regulation problems, particularly in the cancer treatment by considering nonlinear mathematical models of tumor growth. Babaei and Salamci addressed the personalized drug delivery protocol for patients with unknown parameters or mathematical models. One of the shortcomings of the proposed SDRE‐based MRAC method may be the local stability, in line with the nature of the SDRE technique.…”

Section: Introductionmentioning

confidence: 99%

Controller design for personalized drug administration in cancer therapy: Successive approximation approach

Babaei

Optim Control Appl Methods

2017

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Summary A novel controller design approach is developed to determine personalized chemotherapy drug delivery protocols for cancer treatment. The methodology combines successive approximation approach for optimal control and model reference adaptive control to realize the proposed drug administration scenario for patients without prior knowledge of model parameters in the nonlinear cancer dynamics. Although many approaches have been proposed to determine the optimal drug delivery protocol for eradicating tumor in the nonlinear cancer model, the main shortcoming of these approaches is the requirement of nonlinear model dynamics, which is unknown to physicians in reality. To overcome this deficiency, we first determine the optimal drug delivery protocol for a reference patient with known mathematical model and parameters via successive approximation approach technique. Then, using the proposed approach, we adapt the reference patient's drug administration scenario to unknown cancer patients by suggesting a new adaptation mechanism for the unknown nonlinear plant dynamics. An efficient and robust approach is proposed here for the physicians to prescribe a personalized chemotherapy protocol for a cancer patient by regulating the drug delivery protocol of a reference patient. The efficacy of the proposed algorithm in eradicating the tumor lumps with different sizes in several patients is verified using numerical simulations in which the unknown parameters are randomly selected in the Monte Carlo approach.

“…The advantages of using nonlinear reference model (instead of LTI one) are studied in [6][7][8]. It is stated that the adaptation of a nonlinear plant response to the response of a nonlinear reference model may be faster than to that of an LTI reference model [6][7][8]. The critical issue is that a stable nonlinear reference model is to be assigned so that the response of a dynamical (presumably nonlinear) system converges to the response of the stable nonlinear reference model.…”

Section: Introductionmentioning

confidence: 99%

“…There are some approaches to create a stable nonlinear reference dynamics such as State Dependent Riccati Equation (SDRE) approach [6,9] and Successive Approximation Approach (SAA) [10][11][12]. Among these publications, SDRE is combined with MRAC in [8] to design personalized drugs for cancer treatment and SAA is extended to MRAC design in [12]. In SAA methodology, the nonlinear system model is approximated as a sequence of linear time varying (LTV) dynamical systems, whose controller can be designed by the classical control approaches developed for the linear systems.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Control Design for Nonlinear Systems via Successive Approximations

Babaei

Volume 1: Aerospace Applications; Advances in Control Design Methods; Bio Engineering Applications; Advances in Non-Linear Cont

Hakan

2017

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The paper presents an approach to the Model Reference Adaptive Control (MRAC) design for nonlinear dynamical systems. A nonlinear reference system is considered such that its response is designed to be stable via Successive Approximation Approach (SAA). Having designed the stable reference model through the SAA, MRAC is then formulated for nonlinear plant dynamics with a new adaptation rule to guarantee the convergence of the nonlinear plant response to that of the response of the nonlinear reference model. The proposed design methodology is illustrated with examples for different case studies.