Adaptive Control of Underactuated Planar Pronking Hexapod

TL;DR

This paper introduces an online adaptive control method for an underactuated hexapod robot, improving stability and robustness in pronking gait despite high parameter uncertainties, inspired by biological adaptation.

Contribution

It proposes a novel model-based adaptive control approach that dynamically updates system parameters to maintain stable gait in underactuated legged robots.

Findings

Adaptive control outperforms non-adaptive controllers under high uncertainties

Systematic simulations demonstrate robustness of the proposed method

The approach enables stable gait without precise parameter knowledge

Abstract

Underactuated legged robots depict highly nonlinear and complex dynamical behaviors that create significant challenges in accurately modeling system dynamics using both first principles and system identification approaches. Hence, it makes a more substantial challenge to design stabilizing controllers. If physical parameters on mathematical models have miscalibrations due to uncertainty in identifying and modeling processes, designed controllers could perform poorly or even result in unstable responses. Moreover, these parameters can certainly change-over-time due to operation and environmental conditions. In that respect, analogous to a living organism modifying its behavior in response to novel conditions, adapting/updating system parameters, such as spring constant, to compensate for modeling errors could provide the advantage of constructing a stable gait level controller without needing "exact" dynamical parameter values. This paper presents an online, model-based adaptive control approach for an underactuated planar hexapod robot's pronking behavior adopted from antelope species. We show through systematic simulation studies that the adaptive control policy is robust to high levels of parameter uncertainties compared to a non-adaptive model-based dead-beat controller.