The Aquila optimizer (AO) algorithm is a nature-inspired optimization algorithm that can be used to determine the optimal parameters of a proportional integral derivative (PID) controller to control the speed of a DC motor. The hunting behavior of Aquila, a bird of prey in the northern hemisphere inspires the AO algorithm. It has been shown to perform well on unimodal and multimodal benchmark optimization problems, outperforming other optimization algorithms such as the seagull optimization algorithm (SOA), marine predators algorithm, and chimp optimization algorithm (ChOA). Aribowo *et al*. (2022) applied the AO algorithm to optimize the PID parameters of DC motor speed control, by performing the following steps:

- Determine the objective function: the objective function is the function to be optimized. In this case, the objective function is the error between the desired and actual motor speeds.
- Initialize the population: the population is the set of candidate solutions to be searched by the AO algorithm. Initialize the population with random values for the PID parameters.
- Population evaluation: evaluate the objective function for each individual in the population.
- Selection: selecting the best individual from the population based on its objective function value.
- Crossover: combining the best individuals to create new offspring.
- Mutation: mutating offspring to introduce new solutions.
- Replacement: replacing the worst individuals in the population with new offspring.
- Repeating steps 3-7 until a stopping criterion is met, such as a maximum number of iterations or a minimum error threshold.

By following these steps, the AO algorithm can be used to optimize the PID parameters to control the speed of a DC motor. Doing so can result in better performance compared to other optimization algorithms.

Optimization of PID parameters for controlling DC motor based on the aquila optimizer algorithmWidi Aribowo, Supari Supari, Bambang Suprianto

This study presents the application of the aquila optimizer (AO) algorithm to determine the parameters of the proportional integral derivative (PID) controller to control the speed of a dc motor. The AO method is inspired by the most popular bird of prey in the northern hemisphere named Aquila. Initially, the proposed AO algorithm is applied to unimodal and multimodal benchmark optimization problems. To get the performance of the AO method, the controller is compared with other methods, namely Seagull optimization algorithm (SOA), marine predators algorithm, giza pyramids construction (GPC), and chimp optimization algorithm (ChOA). The results represent that the AO is promising and shows the effectiveness. Determination of PID parameters using the AO method for dc motor speed control system shows superior performance.

By: I. Busthomi