Dynamic Actuator Selection and Robust State-Feedback Control of Networked Soft Actuators

TL;DR

This paper develops a system-theoretic framework for analyzing and controlling networked soft electromagnetic actuators, addressing the lack of understanding of their performance under real-time control and actuator selection.

Contribution

It introduces a novel electromagnetic soft actuator, models the network dynamics including disturbances, and proposes robust control and actuator selection algorithms with computational methods.

Findings

Successful modeling of networked ESAs considering external disturbances.

Development of robust control strategies for soft actuator networks.

Numerical case studies demonstrating the effectiveness of the proposed methods.

Abstract

The design of robots that are light, soft, powerful is a grand challenge. Since they can easily adapt to dynamic environments, soft robotic systems have the potential of changing the status-quo of bulky robotics. A crucial component of soft robotics is a soft actuator that is activated by external stimuli to generate desired motions. Unfortunately, there is a lack of powerful soft actuators that operate through lightweight power sources. To that end, we recently designed a highly scalable, flexible, biocompatible Electromagnetic Soft Actuator (ESA). With ESAs, artificial muscles can be designed by integrating a network of ESAs. The main research gap addressed in this work is in the absence of system-theoretic understanding of the impact of the realtime control and actuator selection algorithms on the performance of networked soft-body actuators and ESAs. The objective of this paper is to establish a framework that guides the analysis and robust control of networked ESAs. A novel ESA is described, and a configuration of soft actuator matrix to resemble artificial muscle fiber is presented. A mathematical model which depicts the physical network is derived, considering the disturbances due to external forces and linearization errors as an integral part of this model. Then, a robust control and minimal actuator selection problem with logistic constraints and control input bounds is formulated, and tractable computational routines are proposed with numerical case studies.