Data-Driven Control of Soft Robots Using Koopman Operator Theory

• Published in: IEEE transactions on robotics Vol. 37; no. 3; pp. 948 - 961
• Main Authors: Bruder, Daniel, Fu, Xun, Gillespie, R. Brent, Remy, C. David, Vasudevan, Ram
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aerospace electronics; Control methods; Controllers; Delays; Dynamic models; Identification methods; Koopman operator; model learning for control; optimal control; Predictive control; Predictive models; Robot arms; Robot control; Robots; Soft robotics; soft robots; State space models; System identification; Task analysis; Tracking control; Tracking errors
• ISSN: 1552-3098; 1941-0468
• DOI: 10.1109/TRO.2020.3038693

Controlling soft robots with precision is a challenge due to the difficulty of constructing models that are amenable to model-based control design techniques. Koopman operator theory offers a way to construct explicit dynamical models of soft robots and to control them using established model-based control methods. This approach is data driven, yet yields an explicit control-oriented model rather than just a "black-box" input-output mapping. This work describes a Koopman-based system identification method and its application to model predictive control (MPC) design for soft robots. Three MPC controllers are developed for a pneumatic soft robot arm via the Koopman-based approach, and their performances are evaluated with respect to several real-world trajectory following tasks. In terms of average tracking error, these Koopman-based controllers are more than three times more accurate than a benchmark MPC controller based on a linear state-space model of the same system, demonstrating the utility of the Koopman approach in controlling real soft robots.