Garuda - Garba Rujukan Digital

Cancer Treatment Precision Strategies Through Optimal Control Theory Abougarair, Ahmed J.; Oun, Abdulhamid A.; Sawan, Salah I.; Abougard, T.; Maghfiroh, H.
Journal of Robotics and Control (JRC) Vol 5, No 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.22378

Lung cancer is a highly heterogeneous disease, with diverse genetic, molecular, and cellular drivers that can vary significantly between individual patients and even within a single tumor. Though combination therapy is becoming more common in the treatment of cancer, it can be challenging to predict how various treatment modalities will interact and what negative effects they may have on a patient's health, such as increased gastrointestinal toxicities, or neurological problems. This paper aims to regulate immunity to tumor therapy by utilizing optimal control theory (OCT). This research suggests a malignant tumor model that can be regulated with a combination of immunological, vaccine, and chemotherapeutic therapy. The optimal control variables are employed to support the best possible treatment plan with the fewest potential side effects by reducing the production of new tumor cells and keeping the number of normal cells above the average carrying capacity. Also, the study addresses patient heterogeneity, individual variations in tumor biology, and immune responses for both young and old cancer patients. Finding the right doses for a treatment that works is the main goal. To do this, we conducted a comparative analysis of two optimum control approaches: the Single Network Adaptive Critic (SNAC) approach, which directly applies the notion of reinforcement learning to the essential conditions for optimality and the Linear Quadratic Regulator (LQR) methodology. Although the study's results show the promise of precision treatment plans, a number of significant obstacles must be overcome before these tactics can be successfully applied in clinical settings. It will be necessary to make considerable adjustments to the healthcare system's infrastructure in order to successfully offer personalized treatment regimens. This includes enhanced interdisciplinary care coordination methods, safe data management systems.

Cancer Treatment Precision Strategies Through Optimal Control Theory Abougarair, Ahmed J.; Oun, Abdulhamid A.; Sawan, Salah I.; Abougard, T.; Maghfiroh, H.
Journal of Robotics and Control (JRC) Vol. 5 No. 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.22378

Lung cancer is a highly heterogeneous disease, with diverse genetic, molecular, and cellular drivers that can vary significantly between individual patients and even within a single tumor. Though combination therapy is becoming more common in the treatment of cancer, it can be challenging to predict how various treatment modalities will interact and what negative effects they may have on a patient's health, such as increased gastrointestinal toxicities, or neurological problems. This paper aims to regulate immunity to tumor therapy by utilizing optimal control theory (OCT). This research suggests a malignant tumor model that can be regulated with a combination of immunological, vaccine, and chemotherapeutic therapy. The optimal control variables are employed to support the best possible treatment plan with the fewest potential side effects by reducing the production of new tumor cells and keeping the number of normal cells above the average carrying capacity. Also, the study addresses patient heterogeneity, individual variations in tumor biology, and immune responses for both young and old cancer patients. Finding the right doses for a treatment that works is the main goal. To do this, we conducted a comparative analysis of two optimum control approaches: the Single Network Adaptive Critic (SNAC) approach, which directly applies the notion of reinforcement learning to the essential conditions for optimality and the Linear Quadratic Regulator (LQR) methodology. Although the study's results show the promise of precision treatment plans, a number of significant obstacles must be overcome before these tactics can be successfully applied in clinical settings. It will be necessary to make considerable adjustments to the healthcare system's infrastructure in order to successfully offer personalized treatment regimens. This includes enhanced interdisciplinary care coordination methods, safe data management systems.

Enhancing Speed Estimation in DC Motors using the Kalman Filter Method: A Comprehensive Analysis Setiawan, Muhammad Haryo; Ma'arif, Alfian; Rekik, Chokri; Abougarair, Ahmed J.; Mekonnen, Atinkut Molla
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 1 (2024): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i1.26591

The accurate estimation of speed is crucial for optimizing the performance and efficiency of DC motors, which find extensive applications in various domains. However, the presence of noise ripple, caused by interactions with magnetic or electromagnetic fields, poses challenges to speed estimation accuracy. In this article, we propose the implementation of the Kalman Filter method as a promising solution to address these challenges. The Kalman Filter is a recursive mathematical algorithm that combines measurements from multiple sources to estimate system states with improved accuracy. By employing the Kalman Filter, it becomes possible to estimate the true speed of DC motors while effectively reducing the adverse effects of noise ripple. This research focuses on determining the optimal values for the Kalman Filter parameters and conducting experiments on a DC motor to evaluate the performance of the proposed approach. The experimental results demonstrate that the Kalman Filter significantly improves the control of speed oscillations and enhances the stability of the DC motor system. Furthermore, a comprehensive analysis of the system's response and parameter tuning reveals the impact of different parameter combinations on settling time, overshoot, and rise time. By carefully selecting appropriate parameters, the proposed approach contributes to accurate speed estimation and effective control of DC motors, advancing the understanding and application of the Kalman Filter in various relevant fields. Overall, this research provides valuable insights into enhancing the performance and efficiency of DC motors through the integration of the Kalman Filter method.

Robust control and optimized parallel control double loop design for mobile robot Abougarair, Ahmed J.; Elmolihi, Ali S.
IAES International Journal of Robotics and Automation (IJRA) Vol 9, No 3: September 2020
Publisher : Institute of Advanced Engineering and Science

Robots have been used in many applications in the past few decades. Moreover, due to high nonlinearity behavior of these systems, an optimal and robust control design approaches have been considered to stabilize and improve their performance and robustness. The uncertainties of the time delay on the output states of the mobile robot system have a significant influence on the system nominal performance. As a result, the work becomes here to address the influence of these uncertainties on the robot system performance. In order to achieve this objective, the nonlinear controller via sliding mode control (SMC) is designed by selecting a suitable sliding surface dynamics in which the considered robot displacement and tilt angle are sliding on. The lyapunov function is considered here to accomplish the design of the sliding control signals for robot stabilization. Furthermore, the stability of the considered system is guaranteed due to convergence of the lyapunov functions into zero when the state trajectories tend to desired set points. In addition, we consider the trajectory tracking and stabilization of TWBMR system using parallel double loop PID controllers whose controllers gains are tuning via linear quadratic regulator (LQR) approach. Finally, to demonstrate the effectiveness of SMC and PID-LQR design methods, the comparison is carried out when the nominal and uncertain conditions.

Title

Found 4 Documents
Search

Abstract

Abstract

Abstract

Abstract

Title Search

Found 4 Documents Search

Abstract

Abstract

Abstract

Abstract

Title

Found 4 Documents
Search