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Electronics 2016, 5(4), 94; doi:10.3390/electronics5040094

A Soft Sensor Development for the Rotational Speed Measurement of an Electric Propeller

College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, 29 YuDao St., Nanjing 210016, China
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Academic Editor: Sergio Montenegro
Received: 1 November 2016 / Revised: 30 November 2016 / Accepted: 14 December 2016 / Published: 20 December 2016
(This article belongs to the Special Issue Unmanned Aerial Systems/Vehicles (UAS/V) and Drones)
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Abstract

In recent decades, micro air vehicles driven by electric propellers have become a hot topic, and developed quickly. The performance of the vehicles depends on the rotational speed of propellers, thus, improving the accuracy of rotational speed measurement is beneficial to the vehicle’s performance. This paper presents the development of a soft sensor for the rotational speed measurement of an electric propeller. An adaptive learning algorithm is derived for the soft sensor by using Popov hyperstability theory, based on which a one-step-delay adaptive learning algorithm is further proposed to solve the implementation problem of the soft sensor. It is important to note that only the input signal and the commutation instant of the motor are employed as inputs in the algorithm, which makes it possible to be easily implemented in real-time. The experimental test results have demonstrated the learning performance and the accuracy of the soft sensor. View Full-Text
Keywords: soft sensor; rotational speed measurement; adaptive learning; electric propeller; micro air vehicle soft sensor; rotational speed measurement; adaptive learning; electric propeller; micro air vehicle
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Ye, F.; Sheng, S. A Soft Sensor Development for the Rotational Speed Measurement of an Electric Propeller. Electronics 2016, 5, 94.

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