Digital Charge Estimation for Piezoelectric Actuators, an Artificial Intelligence Approach

Mohammadzaheri, Morteza and Ziaiefar, Hamidreza and Ghodsi, Mojtaba (2022) Digital Charge Estimation for Piezoelectric Actuators, an Artificial Intelligence Approach. In: Handbook of Research on New Investigations in Artificial Life, AI, and Machine Learning. IGI Global, pp. 117-140. ISBN 9781799886860

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Abstract

For many piezoelectric actuators and their areas of operating, charge is proportional to the position of the actuator. Thus, for such actuators, estimation of charge is largely considered as an equivalent to position estimation. That is, a charge estimator may replace a costly and troublesome position sensor. Nevertheless, a significant portion of the excitation voltage is wasted for charge estimation. This squandered voltage, not used to deform the actuator, is called voltage drop. A class of charge estimators of piezoelectric actuators have a resistor in series with the actuator and can only work together with a digital processor. These are called digital charge estimators and have been shown to witness the smallest voltage drop compared to other charge estimators. This chapter first proposes a design guide for digital charge estimators of piezoelectric actuators to maximise the accuracy with the smallest possible voltage drop. The chapter then details the use of five different artificial intelligence (AI) techniques to tackle this design problem and assess their effectiveness through even-handed comparison.

Item Type: Book Section
Dates:
DateEvent
10 November 2021Accepted
25 February 2022Published
Uncontrolled Keywords: Piezoelectric Actuator, Charge, Voltage Drop, Precision, Artificial Neural Network, Fuzzy, Radial Basis Function, Fully Connected Cascade Network.
Subjects: CAH00 - multidisciplinary > CAH00-00 - multidisciplinary > CAH00-00-00 - multidisciplinary
CAH10 - engineering and technology > CAH10-01 - engineering > CAH10-01-02 - mechanical engineering
CAH10 - engineering and technology > CAH10-01 - engineering > CAH10-01-08 - electrical and electronic engineering
CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Engineering and the Built Environment
Depositing User: Dr Morteza Mohammadzaheri
Date Deposited: 31 Jan 2022 09:12
Last Modified: 31 Jan 2022 09:12
URI: http://www.open-access.bcu.ac.uk/id/eprint/12732

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