Golden Eagle Algorithm Optimized PID Controller in MATLAB

Golden Eagle Algorithm Optimized PID Controller in MATLAB

Introduction:

PID controllers play a critical role in regulating systems by adjusting control inputs based on error feedback. However, manually tuning these parameters can be time-consuming and challenging. The Golden Eagle algorithm offers a nature-inspired approach to automate this process efficiently.

Golden Eagle Optimization: The Golden Eagle algorithm draws inspiration from the hunting behavior of golden eagles, incorporating factors such as prey selection, attack strategy, and enthusiasm. These principles are translated into mathematical equations to optimize parameters efficiently.

Algorithm Implementation: In our Simulink model, we begin by defining the plant to be controlled by the PID controller. We then incorporate feedback control to compare the plant output with the reference signal. The Golden Eagle algorithm is applied to tune the KP and KD parameters iteratively.

Objective Function: The objective function in our optimization process is the absolute error between the plant output and the reference signal. By minimizing this error, we aim to achieve optimal control performance.

Results and Analysis: Upon execution of the Golden Eagle optimization algorithm, we observe the iterative refinement of KP and KD values. The algorithm iterates through multiple generations, gradually reducing the absolute error until convergence is reached. The final optimized values are obtained, leading to improved control performance.

Conclusion: The utilization of the Golden Eagle algorithm offers a powerful and efficient method for tuning PID controller parameters. By leveraging nature-inspired optimization techniques, we can streamline the process of controller design and enhance system performance.