Modeling and Control of Soft Robots Using the Koopman Operator and Model Predictive Control
This addresses the problem of accurate control for soft robots, which is incremental as it applies an existing theoretical framework to a specific domain.
The paper tackled the challenge of precise control for soft robots by using Koopman Operator Theory to create explicit linear models and applying Model Predictive Control (MPC). The Koopman-based MPC controller outperformed a benchmark linear state-space MPC controller on all real-world trajectory following tasks.
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. A model and MPC controller of a pneumatic soft robot arm was constructed via the method, and its performance was evaluated over several trajectory following tasks in the real-world. On all of the tasks, the Koopman-based MPC controller outperformed a benchmark MPC controller based on a linear state-space model of the same system.