Automatic Gain Tuning of a Momentum Based Balancing Controller for Humanoid Robots

TL;DR

This paper introduces an automatic gain tuning method for a momentum-based humanoid robot balancing controller, enhancing stability and control performance through linearization and constrained gain optimization.

Contribution

It presents a novel technique for automatic gain tuning that enforces symmetry and positive definiteness, improving controller robustness and stability in humanoid robots.

Findings

Successful simulation on iCub robot demonstrating improved balance control

Effective linearization of closed-loop joint dynamics with optimized gains

Enhanced stability through symmetry and positive definiteness constraints

Abstract

This paper proposes a technique for automatic gain tuning of a momentum based balancing controller for humanoid robots. The controller ensures the stabilization of the centroidal dynamics and the associated zero dynamics. Then, the closed-loop, constrained joint space dynamics is linearized and the controller's gains are chosen so as to obtain desired properties of the linearized system. Symmetry and positive definiteness constraints of gain matrices are enforced by proposing a tracker for symmetric positive definite matrices. Simulation results are carried out on the humanoid robot iCub.