Applying Artificial Neural Networks for PID Parameter Tuning

In current PID control trends, applying artificial neural networks (ANNs) for PID parameter tuning represents an advanced approach utilizing artificial intelligence algorithms. This method can achieve higher control precision and improved system robustness. By implementing neural networks, the system can automatically learn and adjust PID parameters (proportional, integral, and derivative gains) through training algorithms like backpropagation, adapting more effectively to various control environments and dynamic system behaviors. Key implementation aspects include designing the neural network architecture (number of layers, neurons) and establishing appropriate training datasets. This approach significantly reduces dependence on traditional PID tuning methods that require extensive professional expertise and empirical knowledge, making PID control more intelligent and reliable. Therefore, applying artificial neural networks for PID parameter tuning represents a promising research direction and practical application field in modern control systems.