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首页> 外文期刊>IEEE / ASME Transactions on Mechatronics >Adaptive Hysteresis Compensation Using Reduced Memory Sequences
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Adaptive Hysteresis Compensation Using Reduced Memory Sequences

机译：使用减少的存储器序列的自适应磁滞补偿

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摘要

Hysteresis in sensors and actuators can often be effectively compensated for by implementing an inverse hysteresis model in series with the sensor or the actuator. However, an apparent problem enters when the hysteresis characteristics vary over time in an unpredicted manner. Here, we derive an adaptive hysteresis compensation method for the case when we only have observations that are very sparse in time and magnitude. Contrary to previous methods it is based on reduced memory sequences and a preidentified initial model, which makes it possible to use only a few adaption parameters. In particular, we investigate the Preisach model (PM). Using a Bernstein polynomial basis for the PM, it is shown that invertibility translates into linear constraints, which ensures that the overall identification problem for the initial PM is convex. The dependence on PM initial conditions may have negative effects on hysteresis compensation and model adaptation. We give general conditions for losing this dependence and also an upper bound for the maximum error it may cause. The method is experimentally applied to a sensor for measurement of torque in a shaft. At times, the shaft is unloaded and consequently the torque can then be independently observed as being zero. This kind of problem leads to a nonlinear parameterization, but with very few parameters to update, which is successfully achieved using an extended Kalman filter. The method essentially removes the effects of hysteresis, fatigue, and aging for the intended use of the sensor.
机译：通过实现与传感器或执行器串联的反滞后模型，通常可以有效地补偿传感器和执行器中的滞后。但是，当磁滞特性以不可预测的方式随时间变化时，就会出现一个明显的问题。在这里，我们推导了一种自适应滞后补偿方法，适用于只有时间和幅度都非常稀疏的情况。与以前的方法相反，它基于减少的存储序列和预先确定的初始模型，这使得仅使用一些适配参数成为可能。特别是，我们调查了Preisach模型（PM）。使用PM的伯恩斯坦多项式基础，证明了可逆性转化为线性约束，这确保了初始PM的总体识别问题是凸的。对PM初始条件的依赖可能对磁滞补偿和模型自适应产生负面影响。我们给出了失去这种依赖性的一般条件，并给出了可能导致的最大误差的上限。该方法被实验性地应用于用于测量轴中的扭矩的传感器。有时，轴会空载，因此可以独立地观察到扭矩为零。这种问题会导致非线性参数化，但需要更新的参数很少，这是使用扩展卡尔曼滤波器成功实现的。该方法从根本上消除了传感器的预期用途的滞后，疲劳和老化的影响。

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