PID Neural Network-Based System Control Algorithm

The PID neural network-based system control algorithm is widely applied in engineering fields. This algorithm combines PID controllers with neural networks to achieve more precise and adaptive system control. To further optimize the PID neural network control algorithm, the Particle Swarm Optimization (PSO) algorithm can be employed for parameter tuning, enhancing system performance and control accuracy. Additionally, introducing a momentum term into the PID neural network algorithm improves system stability and response speed. The parameter-based PID neural network approach allows dynamic adjustment of system parameters to accommodate various engineering requirements. Implementation typically involves MATLAB's Neural Network Toolbox for network construction, with custom PID error calculations integrated into the training process. The PSO optimization can be coded using population-based search algorithms to minimize performance indices like ISE or IAE. Momentum term implementation requires modifying the weight update rule with velocity components to prevent local minima. These enhanced PID neural network algorithms demonstrate significant potential for industrial applications and can be further refined through additional optimization techniques.