Machine Learning Amplified Control System for HASEL Actuator Soft Robot System

The HASEL actuator is a cutting-edge soft robot compound that is well suited for tasks in unstructured, dynamic environments and has the penitential for superiorly comfortable and smooth human-robot Interaction. However, the nonlinear relation between the input voltage, output strain of the actuators, and the difficulty of analytical modelling makes it hard to design its control software due to the various source of kinematic noises. Machine learning technics, however, which are invented to study the implicit relations in multiparameter problems that do not require pre-existing knowledge, are well suited for HASEL actuators. Traditionally, researchers consider the behavior of this time-dependent system as a sequence of consecutive statuses and use machine learning to enhance conventional algorithms that consume previous and current status and target and adjust the system using varying control input. However, HASEL actuators’ unique propriety of self-stable and negligible lag in response to input changing makes it possible to consider the spatial path of the structure as a whole and control it based on pattern matching. Introducing Recurrent Neural Networks (RNN) and multilayer perceptron (MLP), this paper presents a pattern-matching-based predictive control algorithm for the HASEL actuator system with acceptable size and high accuracy.