Novel Intelligent Optimization Algorithm Based Fractional Order Adaptive Proportional Integral Derivative Controller for Linear Time Invariant based Biological Systems

In recent times, the intelligent biological control system in biomedical engineering has been utilized in various applications such as prosthesis control, drug delivery control, blood glucose control, and heart rate regulation. Moreover, the pancreas, gene regulatory network (GRN), and protein are the main sources in the human body system. However, the parameter regulations of these systems are crucial, because the conventional control methods lack the finest performance, provoking a lot of errors. With an advanced control method, the parameters of the biological system can regulate as per the level to avoid serious conditions. Therefore, in this research, a novel self-constructing intelligent emperor penguin (SIEP) algorithm based fractional-order adaptive proportional integral derivative (FAPID) controller is proposed to regulate the parameters of the system. Here, the FAPID controller gain parameters are effectively tuned by the SIEP algorithm. This proposed SIEP-FAPID controller regulates the constraints of linear time-invariant (LTI) based biological systems such as the pancreas, GRN, and protein formation as per the required level. Moreover, the stability of the controller is studied using the discrete Lyapunov stability analysis function. The quality of the control function has been improved using this proposed approach thus the finest gain and reduced error percentage was obtained. Furthermore, the proposed simulation outcomes are compared with various conventional control approaches which are validated for proving the enhanced controller function under external disturbances. The comparison shows that the proposed SIEP algorithm in the FAPID controller has attained 0.1% less error while comparing with the existing controllers.