AUTHOR=Hyatt Phillip , Johnson Curtis C. , Killpack Marc D. 

TITLE=Model Reference Predictive Adaptive Control for Large-Scale Soft Robots

JOURNAL=Frontiers in Robotics and AI

VOLUME=Volume 7 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2020.558027

DOI=10.3389/frobt.2020.558027

ISSN=2296-9144

ABSTRACT=Past work has shown model predictive control (MPC) to be an effective strategy for controlling continuum joint soft robots using basic lumped-parameter models. However, the inaccuracies of these models often mean that an integral control scheme must be combined with MPC. In this paper we present a novel dynamic model formulation for continuum joint soft robots which is more accurate than previous models yet remains tractable for fast MPC. This model is based on a piecewise constant curvature (PCC) assumption and a relatively new kinematic representation that allows for computationally efficient state prediction. However, due to the difficulty in determining model parameters (e.g. inertias, damping, and spring effects) as well as effects common in continuum joint soft robots (hysteresis, complex pressure dynamics, etc.), we submit that regardless of the model selected, most model-based controllers of continuum joint soft robots would benefit from online model adaptation. Therefore, in this paper we also present a novel form of adaptive MPC based on model reference adaptive control (MRAC). We show that like MRAC, model reference predictive adaptive control (MRPAC) is able to compensate for ``known unknowns" such as unknown inertias. Our experiments also show that like MPC, MRPAC is also robust to ``unknown unknowns" such as unmodeled forces not represented in the form of the adaptive regressor model. Experiments in simulation and hardware show that MRPAC outperforms MPC and MRAC in most realistic scenarios.