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Estimating the angular velocity of a rigid body moving in the plane from tangential and centripetal acceleration measurements

机译:从切向和向心加速度测量值估计刚体在平面中移动的角速度

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摘要

Two methods are available for the estimation of the angular velocity of a rigid body from point-acceleration measurements: (i) the time-integration of the angular acceleration and (ii) the square-rooting of the centripetal acceleration. The inaccuracy of the first method is due mainly to the accumulation of the error on the angular acceleration throughout the time-integration process, which does not prevent that it be used successfully in crash tests with dummies, since these experiments never last more than one second. On the other hand, the error resulting from the second method is stable through time, but becomes inaccurate whenever the rigid body angular velocity approaches zero, which occurs in many applications. In order to take advantage of the complementarity of these two methods, a fusion of their estimates is proposed. To this end, the accelerometer measurements are modeled as exact signals contaminated with bias errors and Gaussian white noise. The relations between the variables at stake are written in the form of a nonlinear state-space system in which the angular velocity and the angular acceleration are state variables. Consequently, a minimum-variance-error estimate of the state vector is obtained by means of extended Kalman filtering. The performance of the proposed estimation method is assessed by means of simulation. Apparently, the resulting estimation method is more robust than the existing accelerometer-only methods and competitive with gyroscope measurements. Moreover, it allows the identification and the compensation of any bias error in the accelerometer measurements, which is a significant advantage over gyroscopes.
机译:有两种方法可以通过点加速度测量来估算刚体的角速度:(i)角加速度的时间积分和(ii)向心加速度的平方根。第一种方法的不准确性主要是由于在整个时间积分过程中在角加速度上积累了误差,这并不能阻止其成功用于带有假人的碰撞测试,因为这些实验的持续时间不会超过一秒钟。另一方面,由第二种方法引起的误差在整个时间上都是稳定的,但是只要刚体角速度接近零,就变得不准确,这在许多应用中都会发生。为了利用这两种方法的互补性,提出了它们的估计的融合。为此,将加速度计的测量结果建模为被偏置误差和高斯白噪声污染的精确信号。相关变量之间的关系以非线性状态空间系统的形式编写,其中角速度和角加速度是状态变量。因此,借助于扩展的卡尔曼滤波获得了状态向量的最小方差误差估计。通过仿真评估了所提出的估计方法的性能。显然,所得到的估计方法比现有的仅使用加速度计的方法更可靠,并且可以与陀螺仪测量相竞争。而且,它可以识别和补偿加速度计测量中的任何偏差误差,这是优于陀螺仪的一个显着优势。

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