Model-Predictive Control with Inverse Statics Optimization for Tensegrity Spine Robots

TL;DR

This paper introduces two novel control strategies combining model-predictive control and inverse statics optimization for tensegrity spine robots, enabling effective closed-loop control of these complex, high-dimensional structures.

Contribution

It presents the first feasible inverse statics optimization algorithm and integrates it with MPC for controlling tensegrity spine robots, reducing tuning complexity.

Findings

Controllers show noise insensitivity

Low tracking error achieved

Validated on 2D and 3D tensegrity spines

Abstract

Robots with flexible spines based on tensegrity structures have potential advantages over traditional designs with rigid torsos. However, these robots can be difficult to control due to their high-dimensional nonlinear dynamics and actuator constraints. This work presents two controllers for tensegrity spine robots, using model-predictive control (MPC) and inverse statics optimization. The controllers introduce two different approaches to making the control problem computationally tractable. The first utilizes smoothing terms in the MPC problem. The second uses a new inverse statics optimization algorithm, which gives the first feasible solutions to the problem for certain tensegrity robots, to generate reference input trajectories in combination with MPC. Tracking the inverse statics reference input trajectory significantly reduces the number of tuning parameters. The controllers are validated against simulations of two-dimensional and three-dimensional tensegrity spines. Both approaches show noise insensitivity and low tracking error, and can be used for different control goals. The results here demonstrate the first closed-loop control of such structures.