Adaptive Control of Robotic Manipulators Using Minimum Parameter Learning Method Based on RBF Networks

This paper presents a minimum parameter learning method based on Radial Basis Function (RBF) networks for adaptive control of robotic manipulators. The proposed approach achieves higher precision and improved stability in manipulator control systems. The RBF network implementation utilizes Gaussian activation functions with adaptive centers and widths, while the minimum parameter learning algorithm reduces computational complexity by optimizing network parameters through gradient descent methods. We provide a comprehensive MATLAB simulation program to facilitate better understanding and learning of this method's implementation. The program includes: - Complete simulation framework with modular function design - Detailed code annotations explaining each algorithm step - Real-time parameter adaptation using Lyapunov stability theory - Dynamic model implementation with Denavit-Hartenberg parameters - Control law integration with uncertainty compensation - Performance visualization tools for tracking error analysis Key MATLAB functions implemented include: • rbf_network_train() - Handles RBF network training with online parameter adjustment • adaptive_controller() - Implements the core control algorithm with stability guarantees • manipulator_dynamics() - Calculates robot dynamics using Euler-Lagrange equations • stability_analysis() - Monitors system stability through Lyapunov function evaluation Through study and practical experimentation with this program, readers can gain deeper insights into the application advantages and implementation nuances of this adaptive control methodology, including real-time parameter convergence analysis and robustness verification under various operating conditions.