Modeling and Control of Soft Robots Using the Koopman Operator and Model Predictive Control

Understand

Controlling soft robots with precision is a challenge due in large part to the difficulty of constructing models that are amenable to model-based control design techniques.

• Koopman Operator Theory offers a way to construct explicit linear dynamical models of soft robots and to control them using established model-based linear control methods.
• This method is data-driven, yet unlike other data-driven models such as neural networks, it yields an explicit control-oriented linear model rather than just a "black-box" input-output mapping.
• This work describes this Koopman-based system identification method and its application to model predictive controller design.