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The formation of new one with its place and area are updated. Step 7: Function . The forward path function is established with the feed. Step 8: Choosing the best . Among all the iterations based on performance best one is chosen. Step 9: Result . Better results are stored for comparison. The working algorithm of JSO is shown in Fig. 4.
The implementation of proposed technique was implemented using MATLAB/Simulink platform. The efficient performance of the proposed technique was then analyzed with the head box model described in Eq. (1). The evaluation section analyzes the pressure and stock level performance of the paper head box. Paper head box corresponds to the nominal pressure along the error value is originally provided. The proposed FOPID controller exhibits a robust capability to handle disturbances and uncertainties within dynamic systems such as the paper machine headbox. By leveraging fractional calculus, the FOPID controller introduces two additional parameters fractional integral and derivative orders that provide finer control over system dynamics compared to traditional PID controllers. This flexibility allows the FOPID controller to adapt to unexpected changes in system conditions, such as variations in stock flow or pressure levels. The controller’s design inherently smoothens the response to disturbances, minimizing overshoot and settling time while maintaining stability. Additionally, the enhanced robustness of the FOPID controller ensures consistent performance in the presence of parameter variations and external noise. Its adaptability is further enhanced through the use of the Jellyfish Search Optimizer (JSO), which optimizes the controller parameters to suit varying operational conditions. Simulation results demonstrate that the FOPID controller reduces overshoot by 30% and settling time by 20% under transient and steady-state disturbances, ensuring precision in maintaining the desired setpoints. This makes the FOPID controller a reliable solution for managing the inherent uncertainties and complexities of the headbox system, ultimately contributing to improved process efficiency and product quality. Using the proposed FOPID controller with optimization algorithm yields better results for the parameters, in order to have lower frequency oscillations. The implementation with simulation also yields optimum results. The simulation model of the proposed model is depicted in Fig. 5. Performance analysis This section describes the controller’s ability to manage uncertainties within a given system. The proposed system was designed using JSO algorithm to analyze the optimum performance of FOPID controller. The system performance was analyzed using sensitivity functions. The analysed parameters of JSO-based FOPID controller
Fig. 4 . Behavior of jelly fish in sea.
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(2025) 15:1631
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