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State estimation of an electro-pneumatic gearbox actuator

机译:电动气动齿轮箱执行器的状态估计

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This paper presents two state estimator algorithms, a Kalman and an Extended Kalman filter in order to determine the chamber pressures of a pneumatic actuator without the application of pressure sensors. The presented state estimators were validated against laboratory measurements, then it was shown, that in applications with high computational resources, such as simulations both algorithms can provide acceptable results and they have nearly the same accuracy. Meanwhile, in embedded systems with higher realizable sample time the Kalman filter, which is based on the linearized state-space representation of the system, can not handle the nonlinear behavior of the actuator in every test case. Based on the validation results, a suggestion was made in order to further improve the accuracy of the presented methods.
机译:本文呈现了两个状态估计器算法,卡尔曼和扩展卡尔曼滤波器,以便在不施加压力传感器的情况下确定气动执行器的腔室压力。呈现的状态估计经过验证,验证了实验室测量,然后显示了,在具有高计算资源的应用中,例如模拟这两个算法可以提供可接受的结果,并且它们具有几乎相同的准确性。同时,在具有较高可实现的采样时间的嵌入式系统中,卡尔曼滤波器基于系统的线性化状态空间表示,不能在每个测试用例中处理执行器的非线性行为。根据验证结果,提出了建议,以进一步提高所提出的方法的准确性。

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