Offset-free Model Predictive Control: A Ball Catching Application with a Spherical Soft Robotic Arm

This paper presents an offset-free model predictive controller for fast and accurate control of a spherical soft robotic arm. In this control scheme, a linear model is combined with an online disturbance estimation technique to systematically compensate model deviations. Dynamic effects such as material relaxation resulting from the use of soft materials can be addressed to achieve offset-free tracking. The tracking error can be reduced by 35% when compared to a standard model predictive controller without a disturbance compensation scheme. The improved tracking performance enables the realization of a ball catching application, where the spherical soft robotic arm can catch a ball thrown by a human.

[1]  Stefan Schulz,et al.  Compliant Robotics and Automation with Flexible Fluidic Actuators and Inflatable Structures , 2012 .

[2]  Manfred Morari,et al.  Offset free model predictive control , 2007, 2007 46th IEEE Conference on Decision and Control.

[3]  Christopher G. Atkeson,et al.  Physical human interaction for an inflatable manipulator , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  Raffaello D'Andrea,et al.  Quadrocopter ball juggling , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  Jörn Malzahn,et al.  A Multi-Link-Flexible Robot Arm Catching Thrown Balls , 2012, ROBOTIK.

[6]  G. Pannocchia,et al.  Offset-free MPC explained: novelties, subtleties, and applications , 2015 .

[7]  Raffaello D'Andrea,et al.  A Fast and Reliable Pick-and-Place Application with a Spherical Soft Robotic Arm , 2020, 2021 IEEE 4th International Conference on Soft Robotics (RoboSoft).

[8]  Marc D. Killpack,et al.  Simultaneous position and stiffness control for an inflatable soft robot , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[9]  Stephen A. Morin,et al.  Soft Robotics: Review of Fluid‐Driven Intrinsically Soft Devices; Manufacturing, Sensing, Control, and Applications in Human‐Robot Interaction   , 2017 .

[10]  Berthold Bäuml,et al.  Kinematically optimal catching a flying ball with a hand-arm-system , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  Jay H. Lee,et al.  Model predictive control: past, present and future , 1999 .

[12]  Jean-Jacques E. Slotine,et al.  Experiments in Robotic Catching , 1991, 1991 American Control Conference.

[13]  Raffaello D'Andrea,et al.  Design, fabrication, modeling and control of a fabric-based spherical robotic arm , 2020 .

[14]  Marc D. Killpack,et al.  Model Reference Predictive Adaptive Control for Large-Scale Soft Robots , 2020, Frontiers in Robotics and AI.

[15]  Marc D. Killpack,et al.  Configuration Estimation for Accurate Position Control of Large-Scale Soft Robots , 2019, IEEE/ASME Transactions on Mechatronics.

[16]  David Wingate,et al.  Learning nonlinear dynamic models of soft robots for model predictive control with neural networks , 2018, 2018 IEEE International Conference on Soft Robotics (RoboSoft).

[17]  Takayuki Koizumi,et al.  Development of prosthetic arm with pneumatic prosthetic hand and tendon-driven wrist , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[18]  David J. Levine,et al.  Elastomeric passive transmission for autonomous force-velocity adaptation applied to 3D-printed prosthetics , 2018, Science Robotics.

[19]  D. Simon.  Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches , 2006 .

[20]  G. Whitesides,et al.  Pneumatic Networks for Soft Robotics that Actuate Rapidly , 2014 .