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

## TL;DR

This paper presents a data-driven approach using Koopman Operator Theory to model and control soft robots with model predictive control, demonstrating improved performance over traditional linear models in real-world tasks.

## Contribution

It introduces a Koopman-based system identification method for soft robots and applies it to design an effective MPC controller, outperforming benchmark models.

## Key findings

- Koopman-based MPC outperforms linear state-space MPC in trajectory tasks.
- Explicit linear models from Koopman theory enable better control of soft robots.
- The method is validated on a pneumatic soft robot arm in real-world experiments.

## Abstract

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.