Neural Network-Based PID Controller

In this paper, we present a neural network-based PID controller and validate its performance through comprehensive simulations. First, we provide a detailed explanation of PID controller fundamentals and operational mechanisms, including the proportional, integral, and derivative components and their respective roles in error correction. We then demonstrate how neural networks can optimize PID parameters through adaptive learning algorithms, typically implemented using backpropagation with gradient descent to minimize control errors. The optimization process involves training the neural network with historical system response data to automatically tune Kp, Ki, and Kd parameters for improved control performance. Following this, we conduct simulation experiments modeling various control scenarios, where the control system can be implemented using MATLAB/Simulink with custom neural network training scripts. These simulations evaluate the neural network-PID controller's performance under different operating conditions, comparing response time, stability, and disturbance rejection capabilities against conventional PID controllers. Finally, we summarize our key findings regarding the enhanced adaptability and robustness achieved through neural network integration, and discuss potential future research directions including real-time parameter adaptation and multi-objective optimization techniques.