  We present a high-bandwidth, lightweight, and nonlinear output tracking
technique for soft actuators that combines parsimonious recursive layers for
forward output predictions and online optimization using Newton-Raphson. This
technique allows for reduced model sizes and increased control loop frequencies
when compared with conventional RNN models. Experimental results of this
controller prototype on a single soft actuator with soft positional sensors
indicate effective tracking of referenced spatial trajectories and rejection of
mechanical and electromagnetic disturbances. These are evidenced by root mean
squared path tracking errors (RMSE) of 1.8mm using a fully connected (FC)
substructure, 1.62mm using a gated recurrent unit (GRU) and 2.11mm using a long
short term memory (LSTM) unit, all averaged over three tasks. Among these
models, the highest flash memory requirement is 2.22kB enabling co-location of
controller and actuator.
