Gaussian Process-Enhanced, External and Internal Convertible (EIC) Form-Based Control of Underactuated Balance Robots

TL;DR

This paper introduces a Gaussian process-enhanced control method for underactuated balance robots, improving stability and performance of EIC-based control through data-driven dynamic modeling and experimental validation.

Contribution

It proposes an enhanced EIC control framework using Gaussian processes to address uncontrolled motions and guarantee stability in underactuated balance robots.

Findings

Enhanced control guarantees stability and performance

Experimental validation on two robot examples

Addresses uncontrolled robot motions

Abstract

External and internal convertible (EIC) form-based motion control (i.e., EIC-based control) is one of the effective approaches for underactuated balance robots. By sequentially controller design, trajectory tracking of the actuated subsystem and balance of the unactuated subsystem can be achieved simultaneously. However, with certain conditions, there exists uncontrolled robot motion under the EIC-based control. We first identify these conditions and then propose an enhanced EIC-based control with a Gaussian process data-driven robot dynamic model. Under the new enhanced EIC-based control, the stability and performance of the closed-loop system is guaranteed. We demonstrate the GP-enhanced EIC-based control experimentally using two examples of underactuated balance robots.