Super Twisting Sliding Mode Control of Cancer Chemotherapy

Dey, Bhabani Shankar; Bera, Manas Kumar; Roy, Binoy Krishna

doi:10.1109/vss.2018.8460228

Cited by 13 publications

(14 citation statements)

References 16 publications

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“…Some researchers have used the model of tumor growth [22], whereas some have used the model of the chemotherapy process itself [15]. In this work, a model of cell killing upon application of chemotherapy is used [23].…”

Section: Mathematical Modelmentioning

confidence: 99%

“…The initial tumor volume condition can be clinically specified and expressed as v = v 0 . Desired profile of the evolution of tumor cell v d to time (days) can be taken as follows [23]:…”

Section: Mathematical Modelmentioning

confidence: 99%

“…The parameters a = 0.4 and b = 0.01 are considered for simulation purposes. Nominal values of the parameters r, δ, and λ are considered as 0.1, 0.225, and 0.25, respectively [28].…”

Section: B L 1 Optimal Controlmentioning

confidence: 99%

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Chemotherapy for Cancer Treatment: An L1 Optimal Control Approach

Rautela

Neetish

Bora

et al. 2023

Preprint

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Chemotherapy as a cancer treatment has garnered much scientific interest in recent years. Chemotherapy is essential for the elimination of cancer cells to the greatest extent possible. Clinical studies demonstrate a predictable trajectory of tumor volume after chemotherapy. However, the standard dose regimens often utilized in chemotherapy have substantial toxicity and little therapeutic value. Consequently, optimum drug dosage is crucial for effectively reducing the tumor size to a defined level while tracking a predefined course. This work establishes optimum control theory with L1-norm based cost function to discover the inputs that decrease the difference between real tumor growth and goal size to improve treatment efficacy while minimizing the side effects. Because of the bang-off nature of the L1-norm control profile, intermittent drug dosing is possible. This study also compares and contrasts the findings with the cost function based on L2-norm. Simulation findings validate the performance of the proposed control scheme for chemotherapy. Further study is needed to confirm the procedures in clinical situations and optimize the regimens for individual patients.

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Section: Mathematical Modelmentioning

confidence: 99%

“…The initial tumor volume condition can be clinically specified and expressed as v = v 0 . Desired profile of the evolution of tumor cell v d to time (days) can be taken as follows [23]:…”

Section: Mathematical Modelmentioning

confidence: 99%

See 1 more Smart Citation

Chemotherapy for Cancer Treatment: An L1 Optimal Control Approach

Rautela

Neetish

Bora

et al. 2023

Preprint

Self Cite

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“…B.S. Dey et al developed in 2018 a nonlinear active control approach to eliminate the chaotic behaviour of the cancer model [18] [19]. Y. Islam et al (2020) established 3 distinct leukaemia therapy controllers.…”

Section: Introductionmentioning

confidence: 99%

Minimization of Cancer Cells with ATS Fuzzy Controller: An Application to Androgen Deprivation therapy

Dubey

Kumar

2023

Preprint

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Androgen deprivation therapy, also known as hormone therapy, is a standard treatment for prostate cancer, but its 5-year survival rate is only 57 percent. Moreover, the prolonged therapy duration results in increased medication toxicity and drug resistance. A solution to this problem is adaptive therapy, which applies chemotherapy or immunotherapy after hormone therapy is withdrawn. In the proposed work, the idea of adaptive therapy is used by applying nonlinear control algorithms to the proposed ADT model and making the drug dosage based on the control laws that result from applying these algorithms. The overall objective is to reduce the cancer cells to zero in a minimum amount of time to reduce prolonged exposure to drugs. For this, a super-twisting sliding mode controller (STSMC) is applied to reduce the number of androgenindependent (AI) and androgen-dependent (AD) cells. Next, an active control algorithm (ATS)-based Takagi-Sugeno fuzzy controller is introduced and compared to the STSMC design. The ATS fuzzy controller has significantly reduced the therapy duration to two months and is also globally asymptotically stable. Using the Linear Matrix Inequality (LMI) algorithm and the YALMIP toolbox, the controller has been built. Theoretical outcomes are validated using MATLAB and Simulink.

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“…B.S. Dey et al proposed an active control approach to eliminate the chaotic behaviour of the cancer model [18] [19]. Y. Islam et al (2020) established 3 distinct leukaemia therapy controllers.…”

Section: Introductionmentioning

confidence: 99%

Minimization of Cancer Cells with ATS Fuzzy Controller: An Application to Androgen Deprivation therapy

Dubey

Kumar

2023

Preprint

View full text Add to dashboard Cite

Androgen deprivation therapy (ADT) is commonly used to treat prostate cancer, but its 5-year survival rate remains low at 57%. Prolonged ADT treatment can lead to increased toxicity and drug resistance. To address these challenges, this study proposes adaptive therapy that combines chemotherapy or immunotherapy with discontinuation of hormone therapy. The study applies the super-twisting sliding mode control (STSMC) algorithm to the ADT model to implement adaptive dosing based on control laws derived from these algorithms. The main objective is to quickly minimize cancer cells and reduce prolonged drug exposure. An active control algorithm (ATS)-based Takagi-Sugeno fuzzy controller is also introduced and compared to the STSMC design. The ATS fuzzy controller significantly reduces therapy duration to six months while maintaining global asymptotic stability. The controllers are implemented using the Linear Matrix Inequality (LMI) algorithm and the YALMIP toolbox, and their effectiveness is validated through MATLAB and Simulink simulations. This study presents a novel approach to improve prostate cancer treatment outcomes by integrating nonlinear control algorithms and adaptive dosing strategies, aiming to reduce treatment duration and minimize drug exposure, ultimately enhancing patient outcomes in prostate cancer management.

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Super Twisting Sliding Mode Control of Cancer Chemotherapy

Cited by 13 publications

References 16 publications

Chemotherapy for Cancer Treatment: An L1 Optimal Control Approach

Chemotherapy for Cancer Treatment: An L1 Optimal Control Approach

Minimization of Cancer Cells with ATS Fuzzy Controller: An Application to Androgen Deprivation therapy

Minimization of Cancer Cells with ATS Fuzzy Controller: An Application to Androgen Deprivation therapy

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