Golden Eagle Algorithm Overview:
The Golden Eagle algorithm is a nature-inspired metaheuristic optimization algorithm developed based on the hunting behavior of Golden Eagles. This algorithm involves various factors such as spiral motion, prey selection, attack, and enthusiasm, which are emulated through mathematical equations to optimize a given objective function.
Simulink Model and Implementation:
The Simulink model is designed with a simple plant controlled by a PD controller. The output of the plant is compared with a reference signal, and the error is used as the objective function. The Golden Eagle algorithm is employed to dynamically adjust the PD controller gains (KP and KD) to minimize the error. The implementation involves a MATLAB script that initiates the optimization process.
Golden Eagle Algorithm Script:
The MATLAB script begins by setting parameters such as the function number (in this case, 1 for error minimization), population size (50), maximum number of iterations (100), and attack and enthusiasm factors. The script then calls the Golden Eagle optimization function, which internally invokes the Simulink model for each iteration, optimizing the PD controller parameters.
As the optimization process unfolds, the script displays the current iteration number, and you can observe the variation in the plant's output. The algorithm dynamically adjusts the PD controller gains to minimize the error. The process continues until convergence, and the optimal values for KP and KD are obtained.
Results and Conclusion:
The final optimal values for KP and KD, achieved through the Golden Eagle optimization, are presented as 2.81 and 2.60, respectively. These values result in a minimized error between the plant's output and the reference signal. The Simulink model showcases the settling behavior of the system with the tuned PD controller.
In conclusion, this solution demonstrates the efficacy of the Golden Eagle algorithm in tuning PD controller parameters for optimal system performance. The nature-inspired optimization approach proves effective in minimizing errors and improving controller tuning. Thank you for joining us in exploring this innovative application. Don't forget to subscribe to our channel and click the notification bell for updates on upcoming videos. If you have any questions or suggestions, feel free to leave a comment. Until next time, happy optimizing!