Name
Abstract | ||
|---|---|---|
This paper presents an approach to recursively estimate the simplest linear model that approximates the time-varying local behaviors from imperfect ( noisy and incomplete ) measurements in the internet of things ( IoT ) based distributed decision-making problems. We first show that the problem of finding the lowest order model for a multi-input single-output system is a cardinality ( l0 ) optimization problem, known to be NP-hard. To solve the problem a simpler approach is proposed which uses the recently developed atomic norm concept and the modified Frank-Wolfe ( mFW ) algorithm is introduced. Further, the paper computes the minimum data-rate required for computing the models with imperfect measurements. The proposed approach is illustrated on a building heating, ventilation, and air-conditioning ( HVAC ) control system that aims at optimizing energy consumption in commercial buildings using IoT devices in a distributed manner. The HVAC control application requires recursive thermal dynamical model updates due to frequently changing conditions and non-linear dynamics. We show that the method proposed in this paper can approximate such complex dynamics on single-board computers interfaced to sensors using unreliable communication channels. Real-time experiments on HVAC systems and simulation studies are used to illustrate the proposed method. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/JAS.2020.1003126 | IEEE/CAA Journal of Automatica Sinica |
Keywords | DocType | Volume |
Adaptability,distributed decision systems,imperfect measurements,internet of things (IoT),low order model identification | Journal | 7 |
Issue | ISSN | Citations |
3 | 2329-9266 | 1 |
PageRank | References | Authors |
0.35 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bekiroglu, K. | 1 | 11 | 5.32 |
| S. Seshadhri | 2 | 12 | 8.22 |
| Ethan Png | 3 | 1 | 0.35 |
| Rong Su | 4 | 318 | 45.41 |
| Constantino M. Lagoa | 5 | 164 | 25.38 |