Neural Network Control of First-Order Inverted Pendulum System with MATLAB Simulink Simulation

This project demonstrates the application of neural networks for controlling a first-order inverted pendulum system, accompanied by MATLAB Simulink simulations to provide detailed insights into neural network implementation for control systems. Neural networks, which mimic the interconnected neuron structure of the human brain for learning and decision-making, have extensive applications in control engineering. Through neural network implementation, we can achieve precise control of the inverted pendulum to maintain its balance stability. In the MATLAB Simulink simulation environment, we construct a dynamic model to simulate the pendulum's motion behavior while employing neural network controllers to regulate its movement. The simulation typically involves designing a neural network controller using MATLAB's Neural Network Toolbox, where key functions like 'feedforwardnet' or 'narnet' can be utilized to create appropriate network architectures. The implementation may include training the network with backpropagation algorithms using historical pendulum state data (angle and angular velocity) to learn optimal control policies. Such simulations help deepen understanding of neural networks' role in control systems, providing a foundation for further research and development. The Simulink model would typically incorporate neural network blocks connected to pendulum dynamics equations, with parameter tuning through functions like 'train' for network optimization and 'sim' for system simulation. This approach enables validation of control performance under various disturbance conditions.