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. 2025 Jan 24;15(1):3044.
doi: 10.1038/s41598-025-86583-x.

Adaptive neural observer-based output feedback anti-actuator fault control of a nonlinear electro-hydraulic system with full state constraints

Van Du Phan  1 , Hoai Vu Anh Truong  2 , Van Chuong Le  1 , Sy Phuong Ho  1 , Kyoung Kwan Ahn  3
Affiliations

Adaptive neural observer-based output feedback anti-actuator fault control of a nonlinear electro-hydraulic system with full state constraints

Van Du Phan et al. Sci Rep. .

Abstract

This paper proposes an adaptive output feedback full state constrain (FSC) controller based on the adaptive neural disturbance observer (ANDO) for a nonlinear electro-hydraulic system (NEHS) with unmodeled dynamics. The Barrier Lyapunov Functions (BLFs) are utilized to ensure that all states of the system are specified within the constraints, and the approximation ability of radial basis function neural networks (RBFNNs) is used to cope with the unknown nonlinear functions. An adaptive neural compensation disturbance observer is elaborated to estimate the compound disturbance and oil leakage fault, effectively addressing these negative effects. Subsequently, observer-based output feedback command filter scheme is developed to diminish the explosion of complexity in the taking derivative procedure and obtain high precise tracking performance. The convergence of tracking errors into a small region around the equilibrium is demonstrated by the Lyapunov stability theory. Ultimately, simulation, experiment, and comparative studies are provided to further validate the effectiveness of the proposed control approach.

Keywords: Command-filtered; Disturbance observer; Full state constraint; Neural network; Oil leakage fault.

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Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Sketch of NEHS with faults.
Fig. 2
Fig. 2
Schematic of the proposed control strategy.
Fig. 3
Fig. 3
Tracking control results of the three controllers in simulation.
Fig. 4
Fig. 4
Tracking errors of comparison with other methods in simulation.
Fig. 5
Fig. 5
Control input of comparison with other methods in simulation.
Fig. 6
Fig. 6
Results of proposed ANDO in simulation.
Fig. 7
Fig. 7
The weight adaptive laws of NNs in simulation.
Fig. 8
Fig. 8
ISE, SDE, and MDE indicator values in simulation.
Fig. 9
Fig. 9
Testbench platform of the NEHS.
Fig. 10
Fig. 10
Tracking control results of the three controllers in experiment.
Fig. 11
Fig. 11
Tracking errors of comparison with other methods in experiment.
Fig. 12
Fig. 12
Control input of comparison with other methods in experiment.
Fig. 13
Fig. 13
Results of ANDO for sinusoidal compound disturbance in experiment.
Fig. 14
Fig. 14
The weight adaptive laws of NNs in experiment.
Fig. 15
Fig. 15
ISE, SDE, and MDE indicator values in experiment.
See this image and copyright information in PMC

References

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