  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.
