Robot Robust Adaptive Control Simulation

This project aims to develop a robust adaptive control simulation program for robots, designed to enhance the performance of robot dynamic models and their structural configurations. The implementation will begin by programming robot motion simulations using MATLAB or Python, where we'll utilize numerical integration methods to solve dynamic equations and implement control algorithms. Key functions will include dynamic parameter estimation using recursive least squares and adaptive law implementation for real-time parameter adjustments. Following simulation execution, we'll conduct comprehensive analysis and optimization based on the results to refine control strategies. These findings will then be applied to develop advanced control algorithms, incorporating robust control techniques like sliding mode control or H-infinity methods to achieve more precise and stable robot motions. Furthermore, we'll explore practical implementation aspects of these control algorithms in real-world scenarios, including hardware-in-the-loop testing and performance validation under various disturbance conditions. The ultimate objective is to develop a reliable robot control system capable of adapting to diverse robotic structures and application environments, with features for automatic parameter tuning and disturbance rejection capabilities.