Neural Network PID Control Strategy

In this paper, we provide a detailed discussion of neural network PID control strategy. Firstly, we propose a novel single-neuron adaptive PID controller and present its corresponding control model. Subsequently, we explore the learning algorithm for single-neuron adaptive PID control, which forms a highly adaptive PID controller by modifying the connection weight coefficients of the neuron controller. By leveraging the self-learning capability of neural networks, we achieve online tuning of PID control parameters. To validate our theoretical framework, we conducted comprehensive simulation studies using MATLAB software. The implementation typically involves defining neuron activation functions and weight update rules through gradient descent optimization. Through comparative analysis of simulation results between traditional PID controllers and single-neuron adaptive PID controllers, we demonstrate that neural network PID controllers not only simplify parameter adjustment but also exhibit high precision and strong adaptability, achieving satisfactory control performance through automatic parameter optimization algorithms.