Source Code for Controlling Two-Stage Inverted Pendulum using RBF Neural Network and Fuzzy Control Methods

This paper presents a comprehensive approach to controlling a two-stage inverted pendulum system using RBF neural networks and fuzzy control methods. We provide detailed explanations of both methodologies' working principles along with complete source code implementations to facilitate better understanding of the practical application.

First, we explore the RBF neural network implementation. The radial basis function network employs Gaussian activation functions and implements online learning through gradient descent algorithms. Our code demonstrates how to configure the network structure with appropriate hidden layer nodes and adjust connection weights dynamically to handle the pendulum's nonlinear dynamics. Key functions include network initialization, real-time weight updates, and stability verification modules.

Next, we introduce the fuzzy control implementation. This method establishes a rule-based inference system using triangular membership functions and Mamdani-type inference mechanisms. The source code contains fuzzy rule definition modules, fuzzification interfaces, and defuzzification procedures using centroid calculation methods. We demonstrate how to design linguistic variables for pendulum angle and angular velocity control.

Finally, we present an integrated control strategy combining both methodologies. The hybrid system uses RBF for adaptive learning and fuzzy logic for rule-based decision making. Our implementation includes coordination algorithms that manage the interplay between neural network predictions and fuzzy inferences, with synchronization mechanisms ensuring stable control performance. The complete source code package contains modular components for each subsystem and their integration logic.