Abstract

In this study, two modern nature-inspired meta-heuristics, the Cheetah Optimization Algorithm (COA) and the Artificial Hummingbird Algorithm (AHA), are used to optimally tune the Proportional-Integral-Derivative (PID) controller parameters. COA offers a fast and efficient global search strategy inspired by predator-prey dynamics and combines prey localization with swarm-based attack mechanisms, while AHA models the directional memory search and territorial exploration behaviour of hummingbirds, offering a balanced structure between local and global search. Both algorithms were chosen for their potential to produce highly accurate solutions and their fast convergence. In order to evaluate the optimization performance of the algorithms, test functions such as Easom, Michalewicz and Shubert, which are challenging and have multiple local minima, were preferred. With the parameters obtained at the end of the optimization process, PID controllers are designed for three different transfer functions on first and second order dead-time systems and time-delayed systems. In order to comparatively analyse the dynamic characteristics of the systems and the performance of the controllers, the Integrated Time Squared Error (ITSE) function is used as a standard performance metric. The results show that COA and AHA algorithms perform effectively in PID tuning and provide significant improvements according to the system dynamics.

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