Neural Network PID Control for Inverted Pendulum System

Neural Network PID control code for inverted pendulum systems represents an advanced control methodology that employs neural networks to achieve precise system regulation. This approach utilizes neural network training to dynamically control the inverted pendulum system, where the neural network architecture typically consists of input layers (receiving pendulum state variables), hidden layers (processing nonlinear relationships), and output layers (generating PID parameters). The primary advantage lies in the neural network's ability to autonomously adapt controller parameters (proportional, integral, derivative gains) through continuous learning, leading to superior control performance. The implementation commonly involves backpropagation algorithms for network training, where error signals from the pendulum system are used to update network weights iteratively. This method excels at handling nonlinear system characteristics since neural networks inherently capture complex nonlinear mappings between system states and optimal control actions. Consequently, this approach has gained widespread adoption in inverted pendulum control applications and continues to play a vital role in advanced control systems research, with potential extensions to adaptive control frameworks and real-time parameter optimization algorithms.