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

doi:10.1109/ACC.2015.7170859

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

The Hong Kong Polytechnic University

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

2 Citations (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 language	English
Title of host publication	ACC 2015 - 2015 American Control Conference
Publisher	IEEE
Pages	968-973
Number of pages	6
Volume	2015-July
ISBN (Electronic)	9781479986842
DOIs	https://doi.org/10.1109/ACC.2015.7170859
Publication status	Published - 1 Jan 2015
Event	2015 American Control Conference, ACC 2015 - Hilton Palmer House, Chicago, United States Duration: 1 Jul 2015 → 3 Jul 2015

Conference

Conference	2015 American Control Conference, ACC 2015
Country/Territory	United States
City	Chicago
Period	1/07/15 → 3/07/15

ASJC Scopus subject areas

Electrical and Electronic Engineering

More information

10.1109/ACC.2015.7170859

Cite this

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

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.",

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Munoz, F, Sanchez, EN & Deng, S 2015, Discrete-time inverse optimal control for indoor air temperature and humidity in a direct expansion (DX) air conditioning (A/C) system. in ACC 2015 - 2015 American Control Conference. vol. 2015-July, 7170859, IEEE, pp. 968-973, 2015 American Control Conference, ACC 2015, Chicago, United States, 1/07/15. https://doi.org/10.1109/ACC.2015.7170859

Discrete-time inverse optimal control for indoor air temperature and humidity in a direct expansion (DX) air conditioning (A/C) system. / Munoz, Flavio; Sanchez, Edgar N.; Deng, Shiming.
ACC 2015 - 2015 American Control Conference. Vol. 2015-July IEEE, 2015. p. 968-973 7170859.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AU - Deng, Shiming

PY - 2015/1/1

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N2 - 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.

AB - 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.

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DO - 10.1109/ACC.2015.7170859

M3 - Conference article published in proceeding or book

VL - 2015-July

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EP - 973

BT - ACC 2015 - 2015 American Control Conference

PB - IEEE

T2 - 2015 American Control Conference, ACC 2015

Y2 - 1 July 2015 through 3 July 2015

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

Abstract

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