Improved Machine Learning based Approach for Autotuning PID Controller using Genetic Algorithms and Parallel Processing

Abstract

PID controllers are widely applied in approximately 95% of continuous control systems across process industries, making them a cornerstone of control engineering. Despite their widespread use, these controllers are often inadequately tuned. This study proposes an intelligent adaptive Proportional-Integral-Derivative (PID) controller for managing complex, uncertain processes. To enhance the capabilities of traditional PID controllers, an advanced machine learning approach using Deep Neural Networks (DNNs) is implemented. To optimize the configuration of the neural network and reduce computational load, a Genetic Algorithm (GA)-based structural learning technique is used, combined with parallel computing to accelerate training. Simulation results show that the proposed controller achieves an RMSE of 0.70 in the absence of disturbances, outperforming the Standard PID (0.95 RMSE) and Shallow Neural PID (0.74 RMSE). Under a 20 dB SNR disturbance, the proposed approach maintains robust performance with an RMSE of 0.79, compared to the Standard PID (1.10 RMSE) and Shallow Neural PID (0.86 RMSE). These findings highlight the superiority of the proposed innovatively tuned controller over both standard PID controllers and recently introduced intelligent network-based tuners, particularly in the presence of uncertainties.