Finite-time robust motion control of intelligent vehicles based on neural approximation and disturbance estimation
- 1Nantong Open University, Jiangsu, Nantong, China
Abstract
This paper addresses the inherent trade off between rapid convergence and high‑precision disturbance rejection in intelligent vehicle motion control under severe nonlinearities, parametric uncertainties, and time varying external perturbations. A composite finite‑time robust control strategy is proposed by synergistically integrating a nonsingular fast terminal sliding mode (NFTSM) surface, radial basis function neural networks (RBFNN), and a finite time disturbance observer (FTDO). The NFTSM guarantees singularity free finite time convergence of the tracking errors. The RBFNN approximates the slowly varying unknown dynamics, while the FTDO compensates online for the neural approximation residual and fast varying disturbances in a cascaded “coarse to fine” manner. Lyapunov-based Extensive co‑simulations with MATLAB/Simulink, conducted under double lane change and sinusoidal path scenarios with simultaneous road friction drops and stochastic lateral wind gusts, demonstrate that the proposed framework significantly outperforms conventional nonsingular terminal sliding mode control and disturbance observer based sliding mode control in terms of tracking accuracy, convergence speed, and chattering suppression.
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BibTeX
@article{zhang2026finitetime,
title = {{Finite-time robust motion control of intelligent vehicles based on neural approximation and disturbance estimation}},
author = {Lijuan Zhang},
journal = {PLOS One},
year = {2026},
month = nov,
doi = {10.1371/journal.pone.0362314},
url = {https://doi.org/10.1371/journal.pone.0362314},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Finite-time robust motion control of intelligent vehicles based on neural approximation and disturbance estimation
AU - Lijuan Zhang
PY - 2026
DA - 2026/11/06
JO - PLOS One
DO - 10.1371/journal.pone.0362314
UR - https://doi.org/10.1371/journal.pone.0362314
AB - This paper addresses the inherent trade off between rapid convergence and high‑precision disturbance rejection in intelligent vehicle motion control under severe nonlinearities, parametric uncertainties, and time varying external perturbations. A composite finite‑time robust control strategy is proposed by synergistically integrating a nonsingular fast terminal sliding mode (NFTSM) surface, radial basis function neural networks (RBFNN), and a finite time disturbance observer (FTDO). The NFTSM guarantees singularity free finite time convergence of the tracking errors. The RBFNN approximates the slowly varying unknown dynamics, while the FTDO compensates online for the neural approximation residual and fast varying disturbances in a cascaded “coarse to fine” manner. Lyapunov-based Extensive co‑simulations with MATLAB/Simulink, conducted under double lane change and sinusoidal path scenarios with simultaneous road friction drops and stochastic lateral wind gusts, demonstrate that the proposed framework significantly outperforms conventional nonsingular terminal sliding mode control and disturbance observer based sliding mode control in terms of tracking accuracy, convergence speed, and chattering suppression.
SN - 1932-6203
ER -