In biomedical applications, the regulation of biological systems that contain Genetic Regulatory Networks (GRNs), protein formation, and pancreas structure is crucial for maintaining health. Any significant deviations...
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In biomedical applications, the regulation of biological systems that contain Genetic Regulatory Networks (GRNs), protein formation, and pancreas structure is crucial for maintaining health. Any significant deviations from the set values of these systems can lead to serious diseases and potentially death. Hence, there's a pressing need for effective control systems to manage and regulate these biological processes. Therefore, a novel control approach called Normalized Versatile Fuzzy Fractional-Order Proportional-Integral-Derivative (NV-FFOPID) based controller is suggested to address the challenges of regulating parameters within organic systems. This is designed to control the variables of biological systems like nutrient synthesis, pancreatic function, and GRNs within a Linear-Time Invariant (LTI) model. The stability of the proposed control system is analyzed using appropriate stability theories to ensure its robustness and reliability. To implement and validate the proposed approach, a nonlinear dynamic model of the organic system is developed using Simulink / MATLAB. The efficacy of the NV-FFOPID controller is evaluated by comparing it with numerous advanced control methods commonly taken in biomedical applications. Performance metrics such as convergence time, settling time, overshoot, rise time, and overall system error are used for comparison. The results indicate that the projected controller achieves steady-state conditions with minimal error of 1.2725% in the system. Additionally, it exhibits a lower settling time of 3.7 s, an overshoot having 0.5, a convergence time of 10 s, and a rise time of 1.985 s compared to alternative methods. These findings suggest that the developed NV-FFOPID controller effectively regulates parameter variations in biological systems, thereby offering significant advantages in addressing complex biomedical problems. The proposed approach holds promise for improving the management and control of crucial biological processes, con
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