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Neural Networks Based PID Control of Bidirectional Inductive Power Transfer System

Yuan, Xiaofang, Xiang, Yongzhong, Wang, Yan, Yan, Xinggang (2015) Neural Networks Based PID Control of Bidirectional Inductive Power Transfer System. Neural Processing Letters, 43 (3). pp. 837-847. ISSN 1370-4621. E-ISSN 1573-773X. (doi:10.1007/s11063-015-9453-2) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1007/s11063-015-9453-2

Abstract

Inductive power transfer (IPT) systems facilitate contactless power transfer between two sides and across an air-gap, through weak magnetic coupling. However, IPT systems constitute a high order resonant circuit and, as such, are difficult to design and control. Aiming at the control problems for bidirectional IPT system, a neural networks based proportional-integral-derivative (PID) control strategy is proposed in this paper. In the proposed neural PID method, the PID gains, \(K_{P}\), \(K_{I}\) and \(K_{D}\) are treated as Gaussian potential function networks (GPFN) weights and they are adjusted using online learning algorithm. In this manner, the neural PID controller has more flexibility and capability than conventional PID controller with fixed gains. The convergence of the GPFN weights learning is guaranteed using Lyapunov method. Simulations are used to test the effective performance of the proposed controller.

Item Type: Article
DOI/Identification number: 10.1007/s11063-015-9453-2
Subjects: T Technology > TJ Mechanical engineering and machinery > Control engineering
Divisions: Faculties > Sciences > School of Engineering and Digital Arts > Instrumentation, Control and Embedded Systems
Depositing User: Xinggang Yan
Date Deposited: 12 Nov 2015 10:51 UTC
Last Modified: 01 Aug 2019 10:39 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/51751 (The current URI for this page, for reference purposes)
Yan, Xinggang: https://orcid.org/0000-0003-2217-8398
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