A Robust Control Method for the Trajectory Tracking of Hypersonic Unmanned Flight Vehicles Based on Model Predictive Control

Hypersonic unmanned flight vehicles have complex dynamic characteristics, such as nonlinearity, strong coupling, multiple constraints, and uncertainty. Operating in highly complex flight environments, hypersonic unmanned flight vehicles must not only contend with uncertainties and disturbances such as parameter perturbations and noise but also deal with complex task scenarios such as interception and no-fly zone avoidance. These factors collectively pose great challenges on the control performance of the vehicle. To address the challenges of trajectory tracking for the vehicles under complex constraints, this paper proposes a trajectory tracking control method based on model predictive control (MPC). Firstly, a nonlinear dynamic model for hypersonic unmanned flight vehicles is established. Then, a robust model predictive controller is designed and the optimal control law is derived to address the trajectory tracking control problem under complex constraints such as parameter perturbations. Finally, simulation experiments are designed under the conditions of aerodynamic parameter changes in the longitudinal plane and lateral no-fly zone avoidance. The simulation results demonstrate that the vehicle is capable of accurately and rapidly tracking the reference despite aerodynamic parameter perturbations and large-scale lateral maneuvers, thereby validating the effectiveness of the controller.

Authors

Institutions

Publication Details

Journal
Drones
Published
2025-03-20
DOI
https://doi.org/10.3390/drones9030223
Citations
3
Primary Topic
Adaptive Control of Nonlinear Systems
Type
article
Field-Weighted Citation Impact
3.64

Funders

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

A Robust Control Method for the Trajectory Tracking of Hypersonic Unmanned Flight Vehicles Based on Model Predictive Control

Bowen Xu, Yunfan Zhou, Haixia Ding, Xianyu Wu et al.
3 citations
Drones
Adaptive Control of Nonlinear Systems
3.64
article

A Robust Control Method for the Trajectory Tracking of Hypersonic Unmanned Flight Vehicles Based on Model Predictive Control

Bowen Xu, Yunfan Zhou, Haixia Ding, Xianyu Wu, Weiqi Yang
article en
3 citations

Abstract

Hypersonic unmanned flight vehicles have complex dynamic characteristics, such as nonlinearity, strong coupling, multiple constraints, and uncertainty. Operating in highly complex flight environments, hypersonic unmanned flight vehicles must not only contend with uncertainties and disturbances such as parameter perturbations and noise but also deal with complex task scenarios such as interception and no-fly zone avoidance. These factors collectively pose great challenges on the control performance of the vehicle. To address the challenges of trajectory tracking for the vehicles under complex constraints, this paper proposes a trajectory tracking control method based on model predictive control (MPC). Firstly, a nonlinear dynamic model for hypersonic unmanned flight vehicles is established. Then, a robust model predictive controller is designed and the optimal control law is derived to address the trajectory tracking control problem under complex constraints such as parameter perturbations. Finally, simulation experiments are designed under the conditions of aerodynamic parameter changes in the longitudinal plane and lateral no-fly zone avoidance. The simulation results demonstrate that the vehicle is capable of accurately and rapidly tracking the reference despite aerodynamic parameter perturbations and large-scale lateral maneuvers, thereby validating the effectiveness of the controller.

DronesVol. 9(3)
National University of Defense Technology (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 8%
Adaptive Control of Nonlinear Systems
3.64
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.