Continuous-time Hammerstein model identification utilizing hybridization of Augmented Sine Cosine Algorithm and Game-Theoretic approach

The widespread use of dynamic systems has greatly simplified various human-operated tasks. However, the complex and nonlinear nature of these systems has posed challenges in determining their structure due to heavy reliance on modeling for theoretical and empirical purposes in academic and practical contexts. This challenge is particularly evident in the Hammerstein model, which is well-known for its recognition of structural nonlinearity. To address the limitations of uncovering an optimized continuous-time Hammerstein model, researchers have explored the practical application of the Augmented Sine Cosine Algorithm-Game Theoretic (ASCA-GT). By combining the Game Theoretic (GT) and Sine Cosine Algorithm (SCA), this hybrid approach aims to achieve a nonlinear position-updated process for improved exploration and exploitation, while effectively overcoming issues like local optima trapping. The performance of ASCA-GT was evaluated using 13 benchmark functions and mathematical scenarios, as well as the Twin-Rotor System (TRS) and Electro-Mechanical Positioning System (EMPS). The effectiveness of the proposed technique was assessed using Wilcoxon's rank test, which confirmed the superior performance of ASCA-GT compared to other metaheuristic optimization methods in determining the continuous-time Hammerstein model.