  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.
