High-bandwidth nonlinear control for soft actuators with recursive network models

TL;DR

This paper introduces a lightweight, high-bandwidth nonlinear control method for soft actuators using recursive network models and online optimization, achieving accurate trajectory tracking with minimal computational resources.

Contribution

It proposes a novel control approach combining recursive network models with online Newton-Raphson optimization for improved soft actuator control.

Findings

Achieved root mean squared path tracking errors around 1.6-1.8mm.

Reduced model size enabling high control loop frequencies.

Controller requires minimal flash memory, suitable for co-location with actuators.

Abstract

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.