Detailed Dynamic Model of Antagonistic PAM System and its Experimental Validation: Sensor-less Angle and Torque Control with UKF
This work addresses sensor-less control for PAM systems, which is incremental as it modifies friction models and applies existing UKF methods to a specific robotic domain.
The study tackled the problem of estimating joint angle and torque in an antagonistic pneumatic artificial muscle (PAM) actuator system by proposing a detailed nonlinear mathematical model with an unscented Kalman filter (UKF), achieving estimation accuracy under 7.91% and tracking control performance over 94.75%.
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.