Optimal Augmented Nonlinear PD for Quadruped Robot with Uncertainties

Abstract

This paper presents the Augmented Nonlinear PD (ANPD) controller to address the challenges of quadruped robots’ complex nonlinear dynamics. By integrating adaptive nonlinear augmentation, the controller enhances stability and robustness against uncertainties. The Lyapunov method is demonstrated for analyzing comprehensive stability across uncertainties. Furthermore, the Genetic Algorithm optimizes the parameters of the ANPD controller, ensuring the tuning is more efficient. The results show significant improvements in gait stability, response speed, and adaptability to uneven terrains and external disturbances, making the proposed controller a robust solution for quadruped robotic applications.