Optimization of PID Controllers Using Groupers and Moray Eels Optimization with Dual-Stream Multi-Dependency Graph Neural Networks for Enhanced Dynamic Performance

Traditional Proportional Integral and Derivative (PID) controllers are often utilised in industrial control applications due to their simplicity and ease of implementation. However, their performance can be limited in complex, nonlinear, time-delayed systems, as well as in noisy feedback loops. This study introduces Groupers and Moray Eels Optimization (GMEO) with Dual-Stream Multi-Dependency Graph neural network (DMGNN) to optimize PID controller parameters addressing main challenges like nonlinearity, dynamic adaptation to changing conditions, and robust performance under variable operating conditions. The proposed system combines the GMEO algorithm to optimize the PID gains and the DMGNN model to predict and locally adjust these parameters, ensuring improved accuracy and responsiveness. By dynamically tuning the PID parameters based on current system conditions, the system adapts to varying input voltages and load changes, optimizing application performance. The proposed strategy is assessed and contrasted with existing strategies on the MATLAB platform. The proposed system achieves a significantly reduced settling time of 100 ms, ensuring rapid response and stability under varying load conditions. Additionally, it minimizes overshoot to 1.5% and reduces the steady-state error to just 0.005V, demonstrating superior accuracy and efficiency compared to existing methods. These improvements demonstrate the system’s ability to deliver optimal performance while effectively adapting to dynamic environments, showcasing its superiority over existing techniques.