Least Mean Square Method for LVDT Signal Processing

This paper describes an innovative approach for position estimation using traditional displacement inductive sensors such as linear variable transducers. In addition, the same algorithm offers an evaluation of velocity and acceleration. However, the same approach can be applied to any other kind of alternating-current-excited sensor. The proposed method overcomes the typical limit of traditional processing techniques as coherent demodulation or spectral analysis by implementing a least mean squares estimation of the variables of interest. A working prototype has been designed around a low-cost digital signal processor from Texas Instruments Inc., and an estimation time on the order of 1 ms has been obtained. In static conditions, the resolution is about 0.01% of the full scale of the considered sensor, which is on the same order as the one obtained with spectral estimation. In dynamic conditions, simulations show a performance improvement in position and velocity estimation with a sensible root mean square error (RMSE) reduction. The experimental results in dynamic conditions are difficult to quantify, owing to noise, even if the performances are better than with traditional methods.