Kinematic Control of Humanoid Upper Body Robot Using Virtual Flexible Joint Dynamics Primitive and Quasi-Sliding Mode Observer

Abstract

This article presents an innovative approach to robotic kinematic control through a state-disturbance observer framework. Inspired by the dynamics of the human arm, this study introduces, for the first time, a virtual flexible joint dynamics primitive (VFJDP) and integrates it into the kinematic control of a humanoid upper body robot to generate smooth and precise movements. Two jounce-level control schemes leveraging the VFJDP are developed, along with a novel quasi-sliding mode disturbance observer for state feedback control. The VFJDP-based schemes enable precise trajectory tracking within joint angle and velocity constraints, significantly improving the robot’s manipulability and producing higher order joint commands. Theoretical analysis proves the asymptotic convergence of the observer and control algorithm. Comparative simulations show that the proposed observer improves state and disturbance estimation accuracy by over 80% compared to state-of-the-art methods. Simulations under noisy conditions further verify the robustness of the proposed approach. Furthermore, experiments involving visual servoing tasks validate the VFJDP-based schemes, achieving a 16% improvement in trajectory tracking accuracy and a 20% increase in manipulability compared to existing methods, including the improved clamping weighted least-norm (ICWLN) and zeroing neural network (ZNN) methods. These results confirm the proposed framework’s effectiveness in tackling kinematic control challenges in humanoid robotics.