PAM系统的详细动态模型及其实验验证：基于UKF的无传感器角度和扭矩控制
Detailed Dynamic Model of Antagonistic PAM System and its Experimental Validation: Sensor-less Angle and Torque Control with UKF
摘要：
本研究提出一个详细的非线性数学模型的拮抗性气动人工肌肉（PAM）驱动器系统的关节角度和扭矩估计使用无迹卡尔曼滤波器（UKF）。所提出的模型描述的混合状态空间表示。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。对部分摩擦力模型进行了修正，得到了一种新的库仑摩擦力形式，该摩擦力取决于PAM的内压。为了验证模型，离线和在线UKF估计和无传感器跟踪控制的关节角度和扭矩进行评估的估计精度和跟踪控制性能。估计精度小于7.91%，稳态跟踪控制性能大于94.75%。这些结果表明，所提出的模型是详细的，可以用来作为一个拮抗PAM系统的状态估计器。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
