Discrete-time inverse optimal control for indoor air temperature and humidity in a direct expansion (DX) air conditioning (A/C) system

Flavio Munoz, Edgar N. Sanchez, Shiming Deng

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

1 Citation (Scopus)

Abstract

This paper presents a discrete-time inverse optimal control scheme for trajectory tracking of a direct expansion (DX) air conditioning (A/C) system. A recurrent high order neural network (RHONN) is used to identify the plant model, and based on this model, a discrete-time inverse optimal control law is derived. The neural network learning is performed on-line by Kalman filtering. The proposed scheme has a structure in which the trajectories can be defined hierarchical by a building energy management system. This novel scheme is tested via simulation. The obtained results for trajectory tracking illustrate the effectiveness of the proposed approach.
Original languageEnglish
Title of host publicationACC 2015 - 2015 American Control Conference
PublisherIEEE
Pages968-973
Number of pages6
Volume2015-July
ISBN (Electronic)9781479986842
DOIs
Publication statusPublished - 1 Jan 2015
Event2015 American Control Conference, ACC 2015 - Hilton Palmer House, Chicago, United States
Duration: 1 Jul 20153 Jul 2015

Conference

Conference2015 American Control Conference, ACC 2015
CountryUnited States
CityChicago
Period1/07/153/07/15

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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