“…The sliding mode control (SMC) is presented in paper [5], a farmland fertility algorithm (FFA) in [6] but the FFA is highly reliant on accurate and extensive data which can be challenging to acquire, a genetic algorithm in [7] which requires many iterations and evaluations, making it slow for complex problems like multi-machine system, a particle swarm optimization (PSO) in [8] but this algorithm performance critically depends on fine-tuning parameters, making it complex and less robust, a chaotic sunflower optimization algorithm in [9] which requiring careful tuning of chaotic parameters, struggles with local optima, prioritizing exploitation (refining good solutions) over exploration (finding new, potentially better regions) leading to missed global optima, a moth search algorithm in [10] but it lacks the rigorous mathematical backing of some established optimization methods, raising concerns about stability and global convergence guarantees, bio-inspired algorithms in [11] which is highly dependent on fine-tuning specific parameters, impacting effectiveness and requiring more expertise, a sliding mode control in [12]. Moreover, artificial intelligence-based training and tuning techniques have been used to develop a PSS as a Deep reinforcement learning-based method in [13], a neuro-adaptive predictive control in [14], a fuzzy-based controller in [15][16][17], damped Nyquist plot for the phase and gain optimization in [18] but all these algorithms may require intensive computations compared to simpler algorithms, especially for complex problems.…”