Prescribed-Time Event-Triggered Cooperative Guidance Law for Multiple UAVs Under Switching Topologies and Actuator Delays

To address time-varying communication topology, actuator response delay, and limited inter-UAV communication resources in the multi-UAV approach of a maneuvering target, this paper proposes a cooperative rendezvous/tracking control law combining a prescribed-time extended state observer (PTESO) with a dynamic event-triggered mechanism (DET). A three-state PTESO is designed whose observation error converges, within a prescribed time independent of the initial error, into a compact set related to the disturbance upper bound. Along the line-of-sight (LOS) direction, the remaining flight times of the UAVs are driven to consensus within a prescribed time toward a specified arrival instant via a threshold-adaptive DET; along the LOS normal direction, prescribed-time convergence of the elevation and azimuth angle errors is achieved through a time-varying-gain sliding surface. The guidance gains are designed from the worst-case algebraic connectivity of the candidate topology set, ensuring uniform validity under arbitrary switching. After accounting for first-order autopilot inertial dynamics, the command tracking error is proven uniformly ultimately bounded. Simulations of four UAVs cooperatively approaching a maneuvering non-cooperative object under periodic topology switching and actuator delay show an arrival-time deviation below 0.01 s, a terminal position error under 0.08 m, and 75–91 average inter-UAV triggers, outperforming existing prescribed-time/fixed-time cooperative guidance methods.

Authors

Institutions

Publication Details

Journal
Drones
Published
2026-09-01
DOI
https://doi.org/10.3390/drones10090670
Primary Topic
Guidance and Control Systems
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Prescribed-Time Event-Triggered Cooperative Guidance Law for Multiple UAVs Under Switching Topologies and Actuator Delays

Jikun Ye, Lei Shao, Fuqi Yang, Hao You et al.
Drones
Guidance and Control Systems
article

Prescribed-Time Event-Triggered Cooperative Guidance Law for Multiple UAVs Under Switching Topologies and Actuator Delays

Jikun Ye, Lei Shao, Fuqi Yang, Hao You, Lei Zhang
article en

Abstract

To address time-varying communication topology, actuator response delay, and limited inter-UAV communication resources in the multi-UAV approach of a maneuvering target, this paper proposes a cooperative rendezvous/tracking control law combining a prescribed-time extended state observer (PTESO) with a dynamic event-triggered mechanism (DET). A three-state PTESO is designed whose observation error converges, within a prescribed time independent of the initial error, into a compact set related to the disturbance upper bound. Along the line-of-sight (LOS) direction, the remaining flight times of the UAVs are driven to consensus within a prescribed time toward a specified arrival instant via a threshold-adaptive DET; along the LOS normal direction, prescribed-time convergence of the elevation and azimuth angle errors is achieved through a time-varying-gain sliding surface. The guidance gains are designed from the worst-case algebraic connectivity of the candidate topology set, ensuring uniform validity under arbitrary switching. After accounting for first-order autopilot inertial dynamics, the command tracking error is proven uniformly ultimately bounded. Simulations of four UAVs cooperatively approaching a maneuvering non-cooperative object under periodic topology switching and actuator delay show an arrival-time deviation below 0.01 s, a terminal position error under 0.08 m, and 75–91 average inter-UAV triggers, outperforming existing prescribed-time/fixed-time cooperative guidance methods.

DronesVol. 10(9)
PLA Rocket Force University of Engineering (CN), Air Force Engineering University (CN)
National Natural Science Foundation of China
Peace, Justice and strong institutions
Openalex Percentile: Top 7%
Guidance and Control Systems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.