论文标题
拮抗PAM系统的详细动态模型及其实验验证：无传感器角度和UKF扭矩控制
Detailed Dynamic Model of Antagonistic PAM System and its Experimental Validation: Sensor-less Angle and Torque Control with UKF
论文作者
论文摘要
这项研究提出了一种详细的非线性数学模型的拮抗性气动人造肌肉（PAM）执行器系统，用于使用无用的卡尔曼滤波器（UKF）估算关节角度和扭矩。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，阀的质量流和摩擦模型。修改了摩擦模型的一部分，以根据PAM的内部压力获得新的库仑摩擦形式。对于模型验证，进行了离线和在线UKF估计以及对关节角度和扭矩的无传感器跟踪控制，以评估估计准确性和跟踪控制性能。估计精度小于7.91％，稳态跟踪控制性能超过94.75％。这些结果证实了所提出的模型已详细介绍，可以用作拮抗PAM系统的状态估计器。
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.
