| 摘要: |
| This paper considers the tracking problem of a fleet of unmanned surface vehicles (USVs) subject to low state feedback
frequencies, disturbances, and communication delays. Influenced by the high computational complexity of localization algorithms,
the low-frequency state feedback brings challenges in fulfilling the high-frequency control requirement for USVs.
Therefore, a novel self-triggered distributed model predictive control (ST-DMPC) approach, with a codesign dual-model
control strategy, is proposed. By simultaneously optimizing control inputs and triggering intervals, this approach achieves
expected control performance comparable to high-frequency control under lowstate feedback frequencies. Furthermore, sufficient
conditions for ensuring recursive feasibility and closed-loop system stability are derived. Finally, a numerical experiment
and comparison study are conducted to demonstrate the efficacy of the proposed approach. |
| 关键词: Distributed model predictive control · Self-triggered control · USVs · Trajectory tracking |
| DOI：https://doi.org/10.1007/s11768-025-00310-5 |
|
| 基金项目:This work was supported in part by the National Natural Science
Foundation of China (NSFC) under Grant Nos. U24B20183,
62273281, and U22B2039. |
|
| Triggering and control codesign in robust self-triggered DMPC fortrajectory tracking control ofmulti-USV system |
| Qifan Yang1,Huiping Li1 |
| (School of Marine Science and Technology, Northwestern
Polytechnical University, Xi’an 710072, Shaanxi, China) |
| Abstract: |
| This paper considers the tracking problem of a fleet of unmanned surface vehicles (USVs) subject to low state feedback
frequencies, disturbances, and communication delays. Influenced by the high computational complexity of localization algorithms,
the low-frequency state feedback brings challenges in fulfilling the high-frequency control requirement for USVs.
Therefore, a novel self-triggered distributed model predictive control (ST-DMPC) approach, with a codesign dual-model
control strategy, is proposed. By simultaneously optimizing control inputs and triggering intervals, this approach achieves
expected control performance comparable to high-frequency control under lowstate feedback frequencies. Furthermore, sufficient
conditions for ensuring recursive feasibility and closed-loop system stability are derived. Finally, a numerical experiment
and comparison study are conducted to demonstrate the efficacy of the proposed approach. |
| Key words: Distributed model predictive control · Self-triggered control · USVs · Trajectory tracking |
