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Neuroadaptive output-feedback trajectory tracking control for a stratospheric airship with prescribed performance

Published online by Cambridge University Press:  09 July 2020

Y. Wu
Affiliation:
School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai, 200240, PR China
Q. Wang*
Affiliation:
School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai, 200240, PR China
D. Duan
Affiliation:
School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai, 200240, PR China
W. Xie
Affiliation:
School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai, 200240, PR China
Y. Wei
Affiliation:
School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai, 200240, PR China

Abstract

In this article, we investigate the horizontal trajectory tracking problem for an underactuated stratospheric airship subject to nonvanishing external disturbances and model uncertainties. By transforming the tracking errors into new virtual error variables, we can specify the transient and steady-state tracking performance of the resulting nonlinear system quantitatively, which means that under the proposed control scheme, the tracking errors will converge to prescribed residual sets around the origin before a preselected finite time with decay rates no less than a preassignable value. To address unknown items, minimal learning parameter (MLP) techniques for neural networks (NNs) approximation are employed, which efficaciously relax the computational burden, enhance the robustness against dynamics uncertainties and provide an improved property for disturbances rejection. A finite-time convergent observer (FTCO) is incorporated into the control framework to realise output-feedback control, ensuring that estimation errors are bounded during operation and approach zero within a finite time. Stability analysis proves that all the closed-loop signals are uniformly bounded. The effectiveness and advantages of the proposed control strategy are verified by simulation results.

Type
Research Article
Copyright
© The Author(s), 2020. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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References

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