Abstract
In this paper, we study event-triggered data scheduling for stochastic multi-loop control systems communicating over a shared network with communication uncertainties. We introduce a novel dynamic scheduling scheme which allocates the channel access according to an error-dependent policy. The proposed scheduler deterministically excludes subsystems with lower error values from the medium access competition in favor of those with larger errors. Subsequently, the scheduler probabilistically allocates the communication resource to the eligible entities. We model the overall network-induced error as a homogeneous Markov chain and show its boundedness in expectation over a multi time-step horizon. In addition, analytical upper bound for the associated average cost is derived. Furthermore, we show that our proposed policy is robust against packet dropouts. Numerical results demonstrate a significant performance improvement in terms of error level in comparison with periodic and random scheduling policies.
| Original language | English |
|---|---|
| Title of host publication | 53rd IEEE Conference on Decision and Control,CDC 2014 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| Pages | 2776-2782 |
| Number of pages | 7 |
| Edition | February |
| ISBN (Electronic) | 9781479977468 |
| DOIs | |
| Publication status | Published - 2014 |
| Externally published | Yes |
| Event | 2014 53rd IEEE Annual Conference on Decision and Control, CDC 2014 - Los Angeles, United States Duration: 15 Dec 2014 → 17 Dec 2014 |
Publication series
| Name | Proceedings of the IEEE Conference on Decision and Control |
|---|---|
| Number | February |
| Volume | 2015-February |
| ISSN (Print) | 0743-1546 |
| ISSN (Electronic) | 2576-2370 |
Conference
| Conference | 2014 53rd IEEE Annual Conference on Decision and Control, CDC 2014 |
|---|---|
| Country/Territory | United States |
| City | Los Angeles |
| Period | 15/12/14 → 17/12/14 |
Bibliographical note
Publisher Copyright:© 2014 IEEE.
ASJC Scopus subject areas
- Control and Systems Engineering
- Modelling and Simulation
- Control and Optimization