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.

中文翻译：

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对抗性 PAM 系统的详细动态模型及其实验验证：使用 UKF 进行无传感器角度和扭矩控制

本研究提出了拮抗气动人工肌肉 (PAM) 致动器系统的详细非线性数学模型，用于使用无迹卡尔曼滤波器 (UKF) 估计关节角度和扭矩。所提出的模型在混合状态空间表示中描述。它包括 PAM 的收缩力、关节动力学、压缩空气的流体动力学、阀门的质量流量和摩擦模型。根据 PAM 的内部压力，对摩擦模型的一部分进行了修改，以获得一种新形式的库仑摩擦。对于模型验证，进行离线和在线 UKF 估计以及关节角度和扭矩的无传感器跟踪控制，以评估估计精度和跟踪控制性能。估计精度小于7.91%，稳态跟踪控制性能达94.75%以上。这些结果证实了所提出的模型是详细的，可以用作对抗 PAM 系统的状态估计器。