Recent advancements in control systems have sparked significant interest among researchers aiming to optimize controller performance across dynamic industrial systems. A notable development introduced by researchers is the application of the mountain gazelle optimizer (MGO) to improve the control parameters of both direct current (DC) motors and three-tank liquid level systems. This innovative approach has produced remarkable results, showcasing the potential for enhanced stability and efficiency within these systems.
The essence of the research lies not just in optimizing control parameters but doing so effectively by borrowing mechanisms inspired by the agile movements of mountain gazelles. By leveraging the behavioral strategies of these animals, the MGO modifies proportional-integral-derivative (PID) controller parameters, resulting in impressive time-domain performance metrics.
For the DC motor system, the study reports achieving a rise time of just 0.0478 seconds, with zero overshoot and settling time measured at 0.0841 seconds. Such performance is substantial, especially when compared to the traditional optimization algorithms like the grey wolf optimizer and particle swarm optimization, which have been applied previously. The introduction of MGO offers enhanced responsiveness and stability, addressing many of the limitations associated with existing methods.
Similarly, the three-tank liquid level system also revealed considerable improvements. The MGO-based control strategy produced a rise time of 11.0424 seconds and settled within 60.6037 seconds—demonstrations of the algorithm's adaptability and potential for real-world applications. The research establishes MGO as not only effective but also as a transformative force, pushing the boundaries of how dynamic systems can be controlled.
The performance advancements can be attributed to the newly introduced ZLG performance indicator, which offers comprehensive assessments of control quality. Researchers noted, "The MGO-based approach consistently achieves lower ZLG values, showcasing its adaptability and robustness in dynamic system control and parameter optimization." This aligns with the comprehensive evaluations conducted against competitive algorithms, confirming MGO's superior capabilities.
This research emphasizes the importance of optimizing control parameters within industrial contexts. It highlights not only the immediate applications of MGO for DC motors and liquid level systems but also its broader potential across varied industrial applications, promoting efficiency, stability, and operational effectiveness.
With the MGO showing such favorable results, the study opens doors for future explorations targeting the optimization of other complex systems, which could lead to sustained advancements within the field of control systems engineering.