A Data-driven Sigmoid-based Secretion Rate of Neuroendocrine-PID Control for TRMS System

This paper investigated the implementation of data-driven sigmoid-based secretion rate of neuroendocrine-PID (SbSR-NEPID) within a twin-rotor MIMO system (TRMS), based on the adaptive safe experimentation dynamics (ASED) algorithm. In essence, SbSR-NEPID is developed as a human body-inspired mechanism that promotes accurate and efficient controller structure. The ASED approach was then employed for parameter tuning of the proposed controller, following its role as the data-driven control scheme that tracks error and input control performances. Fundamentally, such a game-theoretic approach would seek optimal parameters through random perturbations of several elements from its controller's parameters. Its application in tracking both performance and computational interval has also gained vast explorations above statistical ground. As such, results obtained from the simulation has demonstrated data-driven SbSR-NEPID control based on the ASED method as a capable approach in tracking the assigned trajectory missions, while yielding exceptional control accuracy beyond the requirement of theoretical assumptions on the plant dynamics.