Source Code for Controlling Double Inverted Pendulum Using RBF Neural Network and Fuzzy Control Methods

This source code implements control of a double inverted pendulum system using RBF neural network and fuzzy control methodologies. The core implementation features an RBF neural network as the primary controller, where network weights are continuously learned and optimized through training algorithms to achieve precise balancing and motion control of the double inverted pendulum. The fuzzy control component is specifically designed to handle the system's nonlinear characteristics, utilizing membership functions and rule bases to enhance overall control performance. Key implementation aspects include: - RBF network initialization with Gaussian activation functions and center selection algorithms - Real-time weight adaptation using gradient descent or similar optimization techniques - Fuzzy inference system with carefully designed input/output variables and rule sets - Integration mechanism combining neural network predictions with fuzzy logic decisions - Stability monitoring and control saturation handling for safe operation The code provides comprehensive comments explaining each functional module, parameter configuration guidelines, and algorithm flow descriptions to facilitate understanding and practical application. Users can modify network architectures, adjust fuzzy rules, and tune control parameters based on specific pendulum characteristics and performance requirements.