Design and implementation of a neural-network-controlled UPS inverter

Xiao Sun, Dehong Xu, Hung Fat Frank Leung, Yousheng Wang, Yim Shu Lee

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

5 Citations (Scopus)


A low-cost analog neural network control scheme for the inverters of Uninterruptible Power Supplies (UPS) is proposed to achieve low total harmonics distortion (THD) output voltage and good dynamic response. Such a scheme is based on learning control law from representative example patterns obtained from two simulation models. One is a multiple-feedback-loop controller for linear loads, and the other is a novel idealized load-current-feedback controller specially designed for nonlinear loads. Example patterns for various loading conditions are used in the off-line training of a selected neural network. When the training is completed, the neural network is used to control the UPS inverter on-line. A simple analog hardware is built to implement the proposed neural network controller, an optimized PI controller is built as well. Experimental results show that the proposed neural-network-controlled inverter achieves lower THD and better dynamic responses than the PI-controlled inverter does.
Original languageEnglish
Title of host publicationIECON Proceedings (Industrial Electronics Conference)
Number of pages6
Publication statusPublished - 1 Dec 1999
EventThe 25th Annual Conference of the IEEE Industrial Electronics Society (IECON'99) - San Jose, CA, United States
Duration: 29 Nov 19993 Dec 1999


ConferenceThe 25th Annual Conference of the IEEE Industrial Electronics Society (IECON'99)
Country/TerritoryUnited States
CitySan Jose, CA

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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