Abstract
In this paper, we propose a new self-triggered formulation of Model Predictive Control for continuous-time linear networked control systems. Our control approach, which aims at reducing the number of transmitting control samples to the plant, is derived by parallelly solving optimal control problems with different sampling time intervals. The controller then picks up one sampling pattern as a transmission decision, such that a reduction of communication load and the stability will be obtained. The proposed strategy is illustrated through comparative simulation examples.
| Original language | English |
|---|---|
| Title of host publication | 2016 IEEE 55th Conference on Decision and Control, CDC 2016 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 3078-3083 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781509018376 |
| DOIs | |
| Publication status | Published - 2016 Dec 27 |
| Event | 55th IEEE Conference on Decision and Control, CDC 2016 - Las Vegas, United States Duration: 2016 Dec 12 → 2016 Dec 14 |
Other
| Other | 55th IEEE Conference on Decision and Control, CDC 2016 |
|---|---|
| Country | United States |
| City | Las Vegas |
| Period | 16/12/12 → 16/12/14 |
ASJC Scopus subject areas
- Artificial Intelligence
- Decision Sciences (miscellaneous)
- Control and Optimization
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Hashimoto, K., Adachi, S., & Dimarogonas, D. V. (2016). Self-triggered Model Predictive Control for continuous-time systems: A multiple discretizations approach. In 2016 IEEE 55th Conference on Decision and Control, CDC 2016 (pp. 3078-3083). [7798730] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC.2016.7798730