Online PID Tuning Using Neural Network Self-Correction and Self-Learning Capabilities

Online PID tuning utilizing neural networks' self-correction and self-learning capabilities significantly enhances system performance and stability. Neural networks can autonomously adjust PID parameters through continuous learning and optimization, adapting to various working environments and requirements. This intelligent control algorithm provides superior handling of system variations and disturbances, achieving more precise and reliable control outcomes. By implementing neural network technology in PID control systems, we enable more efficient and intelligent automation solutions. The implementation typically involves training the neural network with historical process data using backpropagation algorithms, where key functions include real-time error minimization and parameter adaptation through gradient descent optimization. The neural network architecture often employs a multilayer perceptron with hidden layers that process input signals (error, integral, derivative) and output optimized PID gains (Kp, Ki, Kd) through activation functions like sigmoid or ReLU.