Detailed Dynamic Model of Antagonistic PAM System and its Experimental Validation: Sensor-less Angle and Torque Control with UKF

TL;DR

This paper develops a comprehensive nonlinear model of an antagonistic PAM system and validates it experimentally for sensor-less angle and torque control using UKF, achieving high accuracy and steady-state performance.

Contribution

It introduces a detailed hybrid state-space model including novel Coulomb friction dependent on PAM pressure, and demonstrates effective sensor-less control with UKF.

Findings

Estimation accuracy below 7.91%

Steady-state tracking performance exceeds 94.75%

Model validated through offline and online experiments

Abstract

This study proposes a detailed nonlinear mathematical model of an antagonistic pneumatic artificial muscle (PAM) actuator system for estimating the joint angle and torque using an unscented Kalman filter (UKF). The proposed model is described in a hybrid state-space representation. It includes the contraction force of the PAM, joint dynamics, fluid dynamics of compressed air, mass flows of a valve, and friction models. A part of the friction models is modified to obtain a novel form of Coulomb friction depending on the inner pressure of the PAM. For model validation, offline and online UKF estimations and sensor-less tracking control of the joint angle and torque are conducted to evaluate the estimation accuracy and tracking control performance. The estimation accuracy is less than 7.91 %, and the steady-state tracking control performance is more than 94.75 %. These results confirm that the proposed model is detailed and could be used as the state estimator of an antagonistic PAM system.