Double Integral sliding mode control of Leukemia Therapy

Islam, Yasir; Ahmad, Iftikhar; Zubair, Muhammad; Shahzad, Khurram

doi:10.1016/j.bspc.2020.102046

Cited by 30 publications

(31 citation statements)

References 17 publications

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“…The chattering present in SMC when used in automated ADP may affect the performance of linear-EMA, resulting in low control precision, severe wearing off of moving components, and large heat losses in power circuits. Many variants of chattering alleviation techniques for the SMC are discussed in [30,31]. SSMC and ISMC are the most straightforward designs among them.…”

Section: Smcmentioning

confidence: 99%

An Automated Ambulatory Duodopa Pump: A Sliding Mode Control Approach

Malangandi

Jacob

Kunnel

2022

Preprint

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Around ten million people in the world are affected by Parkinson's disease (PD), accounting for about less than 1% of the global population. The rising prevalence of PD draws attention to the growing individual and social cost, as well as the urgent need for efforts to enhance the quality of life of the patient. The automation of the conventional ambulatory Duodopa pump (ADP) is the most challenging aspect of dopamine therapy for PD due to a range of unanticipated external events. In this regard, the goal of this paper is to develop a robust control strategy specifically, a sliding mode controller (SMC) scheme for a newly designed ADP to ensure optimal drug therapy. The proposed ADP is modeled by cascading the mathematical model of linear electromechanical actuator (EMA), drug infusing syringe pump in tandem with the dose-effect relationship of Levodopa and continuous dopamine sensor (CDS). While traditional SMC is liable to induce numerical chattering, smooth SMC (SSMC) and an integral SMC (ISMC) are designed to minimize chattering by maintaining accuracy in set-point tracking of plasma dopamine concentration. The in-silico results described in this paper persuasively confirm that the SSMC and ISMC supersede the traditional one in numerous key ways: chattering in the control input is nearly non-existent, fast-tracking performance, and good transient response. A robust stability evaluation with ISMC reveals that it can accommodate only a small cohort with less inter-patient variability, but its overall performance in terms of chattering reduction and set-point tracking is superior to that of traditional SMC and SSMC.

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Section: Smcmentioning

confidence: 99%

An Automated Ambulatory Duodopa Pump: A Sliding Mode Control Approach

Malangandi

Jacob

Kunnel

2022

Preprint

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“…Regarding the trajectory prediction research of mobile robots, Islam et al combined differential flatness characteristics and integral sliding mode control and considered the dynamic characteristics of wheel actuators, which could realize trajectory tracking control of single-wheel mobile robots [ 12 ]. Barzegar-Kalashani et al used a complete sliding mode controller for the first time in the research on trajectory tracking control of a two-wheeled mobile robot, which could effectively reduce the influence of uncertain factors and achieve a good tracking effect [ 13 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Predictive Control of the Mobile Robot under the Deep Long-Short Term Memory Neural Network Model

Zheng

2022

Computational Intelligence and Neuroscience

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At present, there is a phenomenon of network data packet loss in the trajectory tracking control system, which will degrade or even destabilize the system’s performance. Therefore, this work first explains the theory of the deep long-short term memory (LSTM) neural network model, the kinematic model of mobile robots, and the trajectory tracking error model. The reasons for data packet loss in the control system are analyzed. Second, a prediction model based on the LSTM network is designed according to the theory mentioned above. Finally, the training effect of the LSTM model and the robot trajectory tracking effect based on the model are tested by setting up simulation experiments. The research results are as follows: (1) The pose test error of the mobile robot will eventually tend to zero through the simulation curve generated by the pose parameters (x, y, θ) of the mobile robot. (2) The trajectory tracking error of the deep LSTM neural network prediction and compensation method with the packet loss rate of 5% is less than that with the packet loss rate of 10%. (3) The linear velocity υ of the mobile robot based on the prediction model of the LSTM network varies greatly but is always in the interval (−2, 2). Its angular velocity ω initially fluctuates greatly but gradually tends to zero after about 13 s. (4) When the prediction model tracks the trajectory of the robot, the horizontal position x, the vertical position y, and the angle θ coincide with the reference trajectory. The exploration is conducted to provide a reference for the research on data packet loss in the networked mobile robot trajectory tracking system.

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“…Actually, this term indicates that the population of cancer cells at time t is depended on total dose of injected antiCD25(τ) at time τ ≤ t. The use of integral terms and non-monotonic functions for simulation of kinetic-dynamics of biological phenomena is convenient, for example, in a study, integral terms were used to simulate the dynamics of cytokines [ 39 ]. Also, in other study, Islam et al used sliding model approach to determine anticancer agent dosing and they used non-monotonic functions to simulate anticancer therapeutic effect on cancer cells [ 40 ]. …”

Section: Model Developmentmentioning

confidence: 99%

Prediction of anti-CD25 and 5-FU treatments efficacy for pancreatic cancer using a mathematical model

et al. 2021

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Background Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal disease with rising incidence and with 5-years overall survival of less than 8%. PDAC creates an immune-suppressive tumor microenvironment to escape immune-mediated eradication. Regulatory T (Treg) cells and myeloid-derived suppressor cells (MDSC) are critical components of the immune-suppressive tumor microenvironment. Shifting from tumor escape or tolerance to elimination is the major challenge in the treatment of PDAC. Results In a mathematical model, we combine distinct treatment modalities for PDAC, including 5-FU chemotherapy and anti- CD25 immunotherapy to improve clinical outcome and therapeutic efficacy. To address and optimize 5-FU and anti- CD25 treatment (to suppress MDSCs and Tregs, respectively) schedule in-silico and simultaneously unravel the processes driving therapeutic responses, we designed an in vivo calibrated mathematical model of tumor-immune system (TIS) interactions. We designed a user-friendly graphical user interface (GUI) unit which is configurable for treatment timings to implement an in-silico clinical trial to test different timings of both 5-FU and anti- CD25 therapies. By optimizing combination regimens, we improved treatment efficacy. In-silico assessment of 5-FU and anti- CD25 combination therapy for PDAC significantly showed better treatment outcomes when compared to 5-FU and anti- CD25 therapies separately. Due to imprecise, missing, or incomplete experimental data, the kinetic parameters of the TIS model are uncertain that this can be captured by the fuzzy theorem. We have predicted the uncertainty band of cell/cytokines dynamics based on the parametric uncertainty, and we have shown the effect of the treatments on the displacement of the uncertainty band of the cells/cytokines. We performed global sensitivity analysis methods to identify the most influential kinetic parameters and simulate the effect of the perturbation on kinetic parameters on the dynamics of cells/cytokines. Conclusion Our findings outline a rational approach to therapy optimization with meaningful consequences for how we effectively design treatment schedules (timing) to maximize their success, and how we treat PDAC with combined 5-FU and anti- CD25 therapies. Our data revealed that a synergistic combinatorial regimen targeting the Tregs and MDSCs in both crisp and fuzzy settings of model parameters can lead to tumor eradication.

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Double Integral sliding mode control of Leukemia Therapy

Cited by 30 publications

References 17 publications

An Automated Ambulatory Duodopa Pump: A Sliding Mode Control Approach

An Automated Ambulatory Duodopa Pump: A Sliding Mode Control Approach

Predictive Control of the Mobile Robot under the Deep Long-Short Term Memory Neural Network Model

Prediction of anti-CD25 and 5-FU treatments efficacy for pancreatic cancer using a mathematical model

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