Control of Pneumatic Artificial Muscles with SNN-based Cerebellar-like Model

TL;DR

This paper presents a cerebellar-inspired neural network model based on spiking neurons to control pneumatic artificial muscles in soft robots, improving response and performance in simulation.

Contribution

It introduces a novel SNN-based cerebellar model as a feed-forward controller for PAM-driven soft robots, inspired by biological cerebellar functions.

Findings

The SNN cerebellar model enhances system response.

Simulation shows improved control performance.

The approach effectively manages nonlinearity and uncertainty.

Abstract

Soft robotics technologies have gained growing interest in recent years, which allows various applications from manufacturing to human-robot interaction. Pneumatic artificial muscle (PAM), a typical soft actuator, has been widely applied to soft robots. The compliance and resilience of soft actuators allow soft robots to behave compliant when interacting with unstructured environments, while the utilization of soft actuators also introduces nonlinearity and uncertainty. Inspired by Cerebellum's vital functions in control of human's physical movement, a neural network model of Cerebellum based on spiking neuron networks (SNNs) is designed. This model is used as a feed-forward controller in controlling a 1-DOF robot arm driven by PAMs. The simulation results show that this Cerebellar-based system achieves good performance and increases the system's response.