Abstract
This paper proposes an algorithm for velocity estimation using the position and acceleration signals obtained respectively from a resistive potentiometric displacement sensor and a MEMS accelerometer. The algorithm is composed of two processing chains that independently estimate velocity starting from position and acceleration signals. Velocity estimation from position is obtained through an adaptive windowing differentiator while the estimation from acceleration is based on a leaky integrator low-pass filter. Such two estimations are fused together by means of a tailored weighted average. The proposed algorithm is first simulated in MATLAB and then experimentally implemented and tested. Both simulations and experimental results show that velocity estimation given by the fusion of the outputs of the two processing chains has a lower estimation error compared to the output of each single chain.
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Liu, G.: On velocity estimation using position measurements. In: Proceedings of the 2002 American Control Conference, pp. 1115–1120 (2002)
Brown, R.H., Schneider, S.C., Mulligan, M.G.: Analysis of algorithms for velocity estimation from discrete position versus time data. In: IEEE Transactions on Industrial Electronics, vol. 39, no.1, pp. 11–19 (1992)
Janabi-Sharifi, F., Hayward, V., Chen, C.J.: Discrete-time adaptive windowing for velocity estimation. In: IEEE Transactions on Control Systems Technology, vol. 8, no.6, pp. 1003–1009 (2000)
Zhu, W., Lamarche, T.: Velocity estimation by using position and acceleration sensors. In: IEEE Transactions on Industrial Electronics, vol. 54, no.5, pp. 2706–2715 (2007)
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Mazzoli, F., Alghisi, D., Ferrari, V. (2023). Algorithm for Velocity Estimation in a Multivariable Motion Sensor. In: Di Francia, G., Di Natale, C. (eds) Sensors and Microsystems. AISEM 2022. Lecture Notes in Electrical Engineering, vol 999. Springer, Cham. https://doi.org/10.1007/978-3-031-25706-3_26
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DOI: https://doi.org/10.1007/978-3-031-25706-3_26
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