PID Control Algorithm Based on BP Neural Network

The PID control algorithm based on BP neural network enables effective tracking of sinusoidal, square wave, and step signals. This algorithm operates by comparing input signals with target signals and dynamically adjusting PID controller parameters based on the comparison results to achieve precise tracking of various signal types. In implementation, the BP neural network typically uses a three-layer structure (input, hidden, and output layers) where the input layer receives error signals between the target and actual output, while the output layer generates optimized PID parameters (proportional, integral, and derivative gains). Specifically, when processing sinusoidal signals, the algorithm adapts the PID controller's gain and offset according to the target signal's frequency and amplitude characteristics, minimizing the difference between output and target signals. For square wave and step signals, the algorithm performs corresponding parameter adjustments based on signal features like rise time and overshoot requirements. Key functions in MATLAB implementation would include: - bp_pid_training() for neural network weight optimization using gradient descent - signal_comparison() for real-time error calculation between reference and output signals - parameter_adaptation() for dynamic PID gain adjustments based on neural network outputs The integration of BP neural network's learning capability with PID control's stability makes this algorithm particularly suitable for applications requiring adaptive control systems with varying signal characteristics.