Trajectory tracking control of unmanned aerial vehicle with disturbance observer and robust adaptive neural dynamic surface
4 affiliations
- 1Guangzhou College of Technology and Business, Guangzhou, China
- 2School of Information Engineering, Henan Mechanical and Electrical Vocational College, Zhengzhou, China
- 3School of Economics and Management, Sanming University, Sanming, China
- 4Nantong Normal College, Nantong, China
Abstract
This paper investigates the trajectory tracking control problem for a class of uncertain strict feedback nonlinear systems subject to unknown dynamics and time varying external disturbances, with a specific application to unmanned aerial vehicle (UAV) longitudinal motion. A novel robust adaptive neural dynamic surface control (DSC) scheme integrated with nonlinear disturbance observers (DOB) is proposed. First, nonlinear disturbance observers are systematically constructed for each subsystem to provide real-time estimates of unknown bounded disturbances, and these estimates are explicitly incorporated into both virtual and actual control laws for active compensation, significantly enhancing disturbance rejection capability. Second, radial basis function (RBF) neural networks are employed to approximate the unknown continuous functions arising from system dynamics, and a parameter aggregation strategy is adopted to reduce the number of online adaptation parameters, thereby simplifying the implementation. Third, the dynamic surface control technique is utilized to overcome the “explosion of complexity” inherent in conventional backstepping designs, eliminating the need for analytical differentiation of virtual control laws. Numerical simulations demonstrate superior tracking accuracy and disturbance rejection capability compared to conventional adaptive neural DSC without disturbance observers, validating the effectiveness and robustness of the proposed scheme.
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BibTeX
@article{pan2026trajectory,
title = {{Trajectory tracking control of unmanned aerial vehicle with disturbance observer and robust adaptive neural dynamic surface}},
author = {Xian Pan and Dongxue Wang and Youwu Liu and Xiang Nan},
journal = {PLOS One},
year = {2026},
month = sep,
doi = {10.1371/journal.pone.0358433},
url = {https://doi.org/10.1371/journal.pone.0358433},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Trajectory tracking control of unmanned aerial vehicle with disturbance observer and robust adaptive neural dynamic surface
AU - Xian Pan
AU - Dongxue Wang
AU - Youwu Liu
AU - Xiang Nan
PY - 2026
DA - 2026/09/18
JO - PLOS One
DO - 10.1371/journal.pone.0358433
UR - https://doi.org/10.1371/journal.pone.0358433
AB - This paper investigates the trajectory tracking control problem for a class of uncertain strict feedback nonlinear systems subject to unknown dynamics and time varying external disturbances, with a specific application to unmanned aerial vehicle (UAV) longitudinal motion. A novel robust adaptive neural dynamic surface control (DSC) scheme integrated with nonlinear disturbance observers (DOB) is proposed. First, nonlinear disturbance observers are systematically constructed for each subsystem to provide real-time estimates of unknown bounded disturbances, and these estimates are explicitly incorporated into both virtual and actual control laws for active compensation, significantly enhancing disturbance rejection capability. Second, radial basis function (RBF) neural networks are employed to approximate the unknown continuous functions arising from system dynamics, and a parameter aggregation strategy is adopted to reduce the number of online adaptation parameters, thereby simplifying the implementation. Third, the dynamic surface control technique is utilized to overcome the “explosion of complexity” inherent in conventional backstepping designs, eliminating the need for analytical differentiation of virtual control laws. Numerical simulations demonstrate superior tracking accuracy and disturbance rejection capability compared to conventional adaptive neural DSC without disturbance observers, validating the effectiveness and robustness of the proposed scheme.
SN - 1932-6203
ER -