H-infinity based Extended Kalman Filter for State Estimation in Highly Non-linear Soft Robotic System

Abstract

Sensor data play a significant role in the control of robotic systems. While soft robotics is promising for operation in unstructured environments, it is difficult to integrate sensors into soft robots because their inherent softness can be disturbed by the use of sensors. One way to overcome this challenge is to use an observer/filter to estimate the variables (states) that would have been measured by those sensors. Nevertheless, applying an observer/filter scheme to a soft robot introduces challenges due to the high non-linearity in its system model. In this paper, a novel $\mathcal{H}_{\infty}$ based Extended Kalman Filter (EKF) is proposed to estimate the states of a soft continuum manipulator and its performances are investigated. The $\mathcal{H}_{\infty}$ -EKF are tested with model simulations of an experimentally validated soft continuum manipulator system with highly nonlinear kinematics and dynamics. The results show that $\mathcal{H}_{\infty}$ -EKF achieves accurate estimations in pneumatic muscle actuator (pMA)'s elongation and manipulator's task space coordinates while estimation result for elongation rate is less satisfactory due to large model uncertainties.