Trajectory Tracking Control of a Flexible Spine Robot, With and Without a Reference Input

TL;DR

This paper explores model-predictive control strategies for trajectory tracking of a flexible spine robot in simulation, comparing controllers with and without reference inputs to improve convergence and accuracy.

Contribution

It introduces two control methods for a flexible spine robot, analyzing their performance and convergence properties in simulation.

Findings

Error converges to zero with smoothing controller without reference input

Controller with reference input shows small errors but does not fully converge

Expected future improvements will enhance controller convergence

Abstract

The Underactuated Lightweight Tensegrity Robotic Assistive Spine (ULTRA Spine) project is an ongoing effort to develop a flexible, actuated backbone for quadruped robots. In this work, model-predictive control is used to track a trajectory in the robot's state space, in simulation. The state trajectory used here corresponds to a bending motion of the spine, with translations and rotations of the moving vertebrae. Two different controllers are presented in this work: one that does not use a reference input but includes smoothing constrants, and a second one that uses a reference input without smoothing. For the smoothing controller, without reference inputs, the error converges to zero, while the simpler-to-tune controller with an input reference shows small errors but not complete convergence. It is expected that this controller will converge as it is improved further.