Dual FOPID-neural network controller based on fast grey wolf optimizer: application to two-inputs two-outputs helicopter - Amar Rezoug; Jamshed Iqbal; Abdelkrim Nemra (Systems Science & Control Engineering)
This research introduces a novel dual Fast Grey Wolf Optimizer (FGWO) combined with Radial Basis Function Neural Networks (RBFNN) for a Fractional-Order PID (FOPID) controller applied to a helicopter simulator. The proposed FGWO improves the standard Grey Wolf Optimizer (GWO) by enhancing hunting during the exploitation phase and increases robustness in convergence to the minimum value. FGWO optimizes the FOPID parameters using a novel objective function. The RBFNN is integrated to address the nonlinearities and uncertainties, while a dual block mitigates the coupling effects. The performance of the proposed controller is characterized by two simulation scenarios. The first scenario involved nine benchmark functions across thirty trials. Results demonstrated that the FGWO offered superior performance in terms of robustness and proximity to the global minimum compared to the GWO. The second scenario involved applying the controllers to the helicopter. Results evidenced that the dual-FOPID-FGWO (DRF-FG) controller achieved a 4.3363% faster response and 1.8199% higher precision than the GWO-based controller (DRF-G). The DRF-FG showed robustness in trajectory tracking compared to the controllers based on the Ant Lion Optimizer (DRF-A) and the Whale Optimization Algorithm (DRF-W). DRF-FG improved the average regulation performance by 1.702% and trajectory tracking by 0.152% compared with DRF-G.