Data Driven Calibration and Control of Compact Lightweight Series Elastic Actuators for Robotic Exoskeleton Gloves.

Abstract

The working principle of a SEA is based on using an elastic material connected serially to the mechanical power source to simulate the dynamic behavior of a human muscle. Due to weight and size limitations of a wearable robotic exoskeleton, the hardware design of the SEA is limited. Compact and lightweight SEAs usually have noisy signal output, and can easily be deformed. This paper uses a compact lightweight SEA designed for exoskeleton gloves to demonstrate immeasurable strain and friction force which can cause an average of 34.31% and maximum of 44.7% difference in force measurement on such SEAs. This paper proposes two data driven machine learning methods to accurately calibrate and control SEAs. The multi-layer perception (MLP) method can reduce the average force measurement error to 10.18% and maximum error to 29.13%. The surface fitting method (SF) method can reduce the average force measurement error to 8.06% and maximum error to 35.72%. In control experiments, the weighted MLP method achieves an average of 0.21N force control difference, and the SF method achieves an average of 0.29N force control difference on the finger tips of the exoskeleton glove.