Spiral-Sooty Tern Optimization Algorithm for Dynamic Modelling of A Twin Rotor System

This paper presents a hybrid Spiral - Sooty Tern Algorithm (SSTA) which is an improved version of the original STOA. A spiral model is incorporated into the Sooty-Tern Optimization Algorithm (STOA) structure. A random switching is utilized to change from random-based to deterministic-based searching operations and vice versa. This is to balance between the exploration and exploitation of all searching agents throughout a feasible search area. For solving a real-world problem, the proposed SSTA algorithm in comparison to STOA is applied to optimize parameters of a linear Autoregressive-Exogenous (ARX) dynamic model for a twin rotor system. The dynamic modelling of the system is challenging in the presence of cross coupling effect between the main and tail rotors. 3000 pairs of captured input-output data from the system are used for the identification and optimization purpose. Result of the test has shown that the SSTA has achieved a better accuracy performance compared to the competing algorithm. For dynamic modelling of the nonlinear system, both SSTA and STOA have acquired a sufficiently good model for the twin rotor system.