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

arXiv: https://arxiv.org/abs/2101.01139 · HTML: https://mlsanity.com/paper/2101.01139/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 13 references that are in the map.
- First posted on arXiv 4 Jan 2021.

## Builds on (most cited first, 3 of 3)

- [Modeling and Control of Soft Robots Using the Koopman Operator and Model Predictive Control](https://mlsanity.com/paper/1902.02827/) (2019)
- [Materials that make robots smart](https://mlsanity.com/paper/1711.00537/) (2017)
- [Embedded Neural Networks for Robot Autonomy](https://mlsanity.com/paper/1911.03848/) (2019)

## Built on by (most cited first, 1 of 1)

- [Toward smart composites: small-scale, untethered prediction and control for soft sensor/actuator systems](https://mlsanity.com/paper/2205.10940/) (2022)

## Abstract (arXiv)

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.