MATLAB Code Implementation of PID Control with Single Neuron Adaptive Algorithm

This MATLAB implementation features a control program based on the single neuron adaptive PID control algorithm. The program enables precise system control and optimization through real-time feedback adjustments, resulting in more stable and accurate system outputs. The single neuron adaptive PID control algorithm represents an advanced control methodology that integrates the learning capabilities of neural networks with the robustness of traditional PID controllers. Implementation typically involves defining neuron activation functions, weight adjustment mechanisms, and PID parameter adaptation rules through MATLAB's control system toolbox and custom coding. Key implementation aspects include: establishing the mathematical model for neuron-based parameter adjustment, designing the learning algorithm for adaptive gains (Kp, Ki, Kd), and creating real-time monitoring functions for system response analysis. The code structure generally consists of initialization modules for PID parameters, neuron weight matrices, and learning rates, followed by main control loops that calculate control signals based on error signals and update neuron weights using gradient descent or similar optimization methods. This program effectively enhances control system performance across various practical applications, ensuring improved stability and reliability. Through MATLAB's simulation environment, users can visualize system responses, tune adaptive parameters, and validate control performance under different operating conditions.