Design of a Humanoid Upper-Body Robot and Trajectory Tracking Control via ZNN with a Matrix Derivative Observer.

Area of Science:

• Robotics
• Control Systems
• Artificial Intelligence

Background:

• Humanoid robots offer advanced capabilities but face challenges in precise trajectory tracking.
• Conventional zeroing neural network (ZNN) controllers rely heavily on Jacobian matrix derivatives, limiting performance.
• Existing robotic systems often have restricted workspaces and tracking accuracy.

Purpose of the Study:

• To present a novel 22-DOF humanoid upper-body robotic system inspired by human biomechanics.
• To introduce an integration-enhanced matrix derivative observer (IEMDO) for ZNN-based trajectory tracking.
• To enhance the workspace and control precision of humanoid robotic arms.

Main Methods:

• Designed a 22-DOF humanoid robotic system using standardized hollow joint modules.
• Developed an IEMDO incorporating nonlinear feedback and integral correction to estimate matrix derivatives.
• Integrated the IEMDO with a ZNN controller for trajectory tracking.

Main Results:

• The proposed humanoid robot achieved an 87.7% larger total workspace and 3.683-fold common workspace expansion.
• The IEMDO demonstrated accurate real-time matrix derivative estimation with high robustness to noise.
• High-precision trajectory tracking was achieved in simulations and real-world experiments.

Conclusions:

• The novel 22-DOF humanoid robot system offers significant improvements in workspace and biomechanical inspiration.
• The IEMDO provides a theoretically sound and practical solution for accurate matrix derivative estimation in ZNN controllers.
• The integrated framework enables high-performance redundant humanoid arm control, advancing the field of humanoid robotics.