Model-Free Trajectory Tracking Control for Nonlinear Non-affine Low-Altitude Vehicle

This paper investigates the trajectory tracking control problem of nonlinear low-altitude vehicle (LAV) in the presence of communication link faults. Existing studies typically rely on the dynamical information of the controlled object. To circumvent unmodeled dynamics, this paper constructs a data- driven mapping of the controlled system based on the compact-form dynamic linearization technique, and proposes an adaptive dynamic estimation mechanism. A data compensation mechanism is also introduced to mitigate the effects of data loss. Moreover, a complete proof of algorithm convergence is provided based on the contraction mapping principle. Finally, simulations demonstrate the effectiveness of the proposed algorithm.

Authors

Institutions

Publication Details

Published
2025-05-23
DOI
https://doi.org/10.1109/icetci64844.2025.11084100
Primary Topic
Vehicle Dynamics and Control Systems
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Model-Free Trajectory Tracking Control for Nonlinear Non-affine Low-Altitude Vehicle

Zhongyang Wang, Zihan Yang
Vehicle Dynamics and Control Systems
article

Model-Free Trajectory Tracking Control for Nonlinear Non-affine Low-Altitude Vehicle

Zhongyang Wang, Zihan Yang
article en

Abstract

This paper investigates the trajectory tracking control problem of nonlinear low-altitude vehicle (LAV) in the presence of communication link faults. Existing studies typically rely on the dynamical information of the controlled object. To circumvent unmodeled dynamics, this paper constructs a data- driven mapping of the controlled system based on the compact-form dynamic linearization technique, and proposes an adaptive dynamic estimation mechanism. A data compensation mechanism is also introduced to mitigate the effects of data loss. Moreover, a complete proof of algorithm convergence is provided based on the contraction mapping principle. Finally, simulations demonstrate the effectiveness of the proposed algorithm.

Beijing University of Chemical Technology (CN)
Openalex Percentile: Top 81%
Vehicle Dynamics 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.