This paper 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 error 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.

In this paper, we propose a method to design a model-following adaptive controller using radial basis function networks (RBF-NNs). The method is very simple to implement by exploiting the property of RBF-NNs. The propased method identifies linear or nonlinear plants and implements a stable model-following adaptive controller by utilizing identification results. Simulation results show the effectiveness of the proposed control schemes. Keywords linear and nonlinear plants, neural networks, radial basis function networks