DC Dual Closed-Loop Speed Regulation System with Neural Network Controller

The DC dual closed-loop speed regulation system is an advanced speed control system that employs neural networks as its core controller. This system achieves precise motor speed regulation by monitoring and analyzing feedback signals while executing control actions based on predefined target values. The system features a straightforward architecture with strong stability characteristics, delivering excellent speed regulation performance across various operating conditions. The neural network controller integrates self-learning and adaptive capabilities, enabling automatic parameter adjustments in response to system variations - this significantly enhances the system's robustness and adaptability. In code implementation, this typically involves two nested control loops: an inner current loop for torque regulation and an outer speed loop for velocity control, with the neural network handling complex non-linear mappings between system states and control outputs. The controller often utilizes backpropagation algorithms for online learning and may employ activation functions like sigmoid or ReLU in hidden layers to process system dynamics. Due to these advantages, DC dual closed-loop speed regulation systems find extensive applications in industrial fields, playing a vital role in improving production efficiency and quality.