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 |
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| 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 | |
| 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 |
Keywords
- Trajectory control
- Air conditioning
- Building management systems
- Discrete time systems
- Energy management systems
ASJC Scopus subject areas
- Electrical and Electronic Engineering