Hybrid Adaptive Control for Series Elastic Actuator of Humanoid Robot

TL;DR

This paper introduces a hybrid adaptive control method combining MRAC and backstepping for humanoid robots with series elastic actuators, enhancing stability and robustness during walking in uncertain environments.

Contribution

It develops a novel control approach integrating MRAC and backstepping to address parameter uncertainties in SEA-driven humanoid walking.

Findings

The proposed control method improves stability during walking.

Experimental results validate robustness against parameter uncertainties.

The approach extends previous research with enhanced control performance.

Abstract

Generally, humanoid robots usually suffer significant impact force when walking or running in a non-predefined environment that could easily damage the actuators due to high stiffness. In recent years, the usages of passive, compliant series elastic actuators (SEA) for driving humanoid's joints have proved the capability in many aspects so far. However, despite being widely applied in the biped robot research field, the stable control problem for a humanoid powered by the SEAs, especially in the walking process, is still a challenge. This paper proposes a model reference adaptive control (MRAC) combined with the backstepping algorithm to deal with the parameter uncertainties in a humanoid's lower limb driven by the SEA system. This is also an extension of our previous research (Lanh et al.,2021). Firstly, a dynamic model of SEA is obtained. Secondly, since there are unknown and uncertain parameters in the SEA model, a model reference adaptive controller (MRAC) is employed to guarantee the robust performance of the humanoid's lower limb. Finally, an experiment is carried out to evaluate the effectiveness of the proposed controller and the SEA mechanism.