RBF Network-Based Simulation Demo for Robotic Arm Motion Joints

This project presents a simulation demonstration for robotic arm motion joints based on RBF (Radial Basis Function) neural networks. The implementation utilizes RBF networks to model and simulate the kinematic behavior of robotic arm joints. In this simulation example, we construct an RBF network architecture to approximate joint dynamics, typically involving input normalization, hidden layer Gaussian activation functions, and linear output layers for joint angle predictions. The simulation framework allows for comprehensive study and understanding of robotic arm joint control mechanisms. Through systematic modeling and simulation, we achieve more precise and refined control over joint movements, thereby enhancing the overall motion performance and functionality of robotic arms. The RBF network implementation generally includes key components such as center selection algorithms (like k-means clustering), width parameter calculation for radial basis functions, and weight optimization through least squares methods. This research provides substantial theoretical and practical foundations for robotic arm design and control systems, contributing to the advancement and application of robotics technology. From a coding perspective, the simulation typically involves MATLAB/Simulink implementations with functions for network training, real-time joint position prediction, and performance validation through trajectory tracking error analysis.