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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

REFERENCES

Azinheira, J.R., de Paiva, E.C., Ramos, J.G. and Beuno, S.S. Mission path following for an autonomous unmanned airship, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065), San Francisco, US, 2000, 2, pp 12691275.Google Scholar
Bechlioulis, C.P. and Rovithakis, G.A.Robust adaptive control of feedback linearizable MIMO nonlinear systems with prescribed performance, IEEE Transactions on Automatic Control, 2008, 53, (9), pp 20902099.CrossRefGoogle Scholar
de Paiva, E.C., Bueno, S.S., Gomes, S.B., Ramos, J.J. and Bergerman, M. A control system development environment for AURORA’s semi-autonomous robotic airship, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C), Detroit, US, 1999, 3, pp 23282335.Google Scholar
Elhaki, O. and Shojaei, K.A robust neural network approximation-based prescribed performance output-feedback controller for autonomous underwater vehicles with actuators saturation, Engineering Applications of Artificial Intelligence, 2020, 88, pp 103382.CrossRefGoogle Scholar
Fu, M., Wang, T. and Wang, C.Adaptive neural-based finite-time trajectory tracking control for underactuated marine surface vessels with position error constraint, IEEE Access, 2019, 7, pp 1630916322.CrossRefGoogle Scholar
Gao, T., Huang, J., Zhou, Y. and Song, Y.D.Robust adaptive tracking control of an underactuated ship with guaranteed transient performance, International Journal of Systems Science, 2017, 48, (2), pp 272279.CrossRefGoogle Scholar
Ge, S.S., Hang, C.C., Lee, T.H. and Zhang, T.Stable Adaptive Neural Network Control, Springer Science & Business Media, Berlin/Heidelberg, Germany, 2013.Google Scholar
Han, D., Wang, X.L., Chen, L. and Duan, D.P.Command-filtered backstepping control for a multi-vectored thrust stratospheric airship, Transactions of the Institute of Measurement and Control, 2016, 38, (1), pp 93104.CrossRefGoogle Scholar
Jin, X.Adaptive fixed-time control for MIMO nonlinear systems with asymmetric output constraints using universal barrier functions, IEEE Transactions on Automatic Control, 2018, 64, (7), pp 30463053.CrossRefGoogle Scholar
Khoury, G.A.Airship Technology, Cambridge University Press, Cambridge, England, 2012.Google Scholar
Lee, Y.G., Kim, D.M. and Yeom, C.H.Development of Korean high altitude platform systems, International Journal of Wireless Information Networks, 2006, 13, (1), pp 3142.CrossRefGoogle Scholar
Li, S., Ma, T., Luo, X. and Yang, Z.Adaptive Fuzzy Output Regulation for Unmanned Surface Vehicles with Prescribed Performance, International Journal of Control, Automation and Systems, 2020, 18, (2), pp 405414.CrossRefGoogle Scholar
Li, Y., Tong, S., Liu, L. and Feng, G.Adaptive output-feedback control design with prescribed performance for switched nonlinear systems, Automatica, 2017, 80, pp 225231.CrossRefGoogle Scholar
Liao, L. and Pasternak, I.A review of airship structural research and development, Progress in Aerospace Sciences, 2009, 45, (4–5), pp 8396.CrossRefGoogle Scholar
Miao, B., Li, T., and Luo, W.A DSC and MLP based robust adaptive NN tracking control for underwater vehicle, Neurocomputing, 2013, 111, pp 184189.CrossRefGoogle Scholar
Park, B.S., Kwon, J.W. and Kim, H.Neural network-based output feedback control for reference tracking of underactuated surface vessels, Automatica, 2017, 77, pp 353359.CrossRefGoogle Scholar
Peng, Z. and Wang, J.Output-feedback path-following control of autonomous underwater vehicles based on an extended state observer and projection neural networks, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017, 48, (4), pp 535544.CrossRefGoogle Scholar
Qin, H., Li, C., Sun, Y. and Wang, N.Adaptive trajectory tracking algorithm of unmanned surface vessel based on anti-windup compensator with full-state constraints, Ocean Engineering, 2020, 200, pp 106906.CrossRefGoogle Scholar
Repoulias, F. and Papadopoulos, E. Robotic airship trajectory tracking control using a backstepping methodology, 2008 IEEE International Conference on Robotics and Automation, IEEE, May 2008, pp 188193.CrossRefGoogle Scholar
Sangjong, L., Lee, H., Daeyeon, W. and Hyochoong, B. Backstepping approach of trajectory tracking control for the mid-altitude unmanned airship, AIAA Guidance, Navigation and Control Conference and Exhibit, 2007, pp 6319.CrossRefGoogle Scholar
Shi, W., Luo, R. and Li, B.Adaptive fuzzy prescribed performance control for MIMO nonlinear systems with unknown control direction and unknown dead-zone inputs, ISA Transactions, 2017, 66, pp 8695.CrossRefGoogle ScholarPubMed
Sun, L. and Zheng, Z.Nonlinear adaptive trajectory tracking control for a stratospheric airship with parametric uncertainty, Nonlinear Dynamics, 2015, 82, (3), pp 14191430.CrossRefGoogle Scholar
Swaroop, D., Hedrick, J.K., Yip, P.P. and Gerdes, J.C.Dynamic surface control for a class of nonlinear systems, IEEE Transactions on Automatic Control, 2000, 45, (10), pp 18931899.CrossRefGoogle Scholar
Wang, D. and Huang, J.Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form, IEEE Transactions on Neural Networks, 2005, 16, (1), pp 195202.CrossRefGoogle ScholarPubMed
Xu, Q., Wang, Z. and Zhen, Z.Adaptive neural network finite time control for quadrotor UAV with unknown input saturation, Nonlinear Dynamics, 2019, 98, (3), pp 19731998.CrossRefGoogle Scholar
Yang, Y.A time-specified nonsingular terminal sliding mode control approach for trajectory tracking of robotic airships, Nonlinear Dynamics, 2018, 92, (3), pp 13591367.CrossRefGoogle Scholar
Yang, Y. and Yan, Y.Neural network gain-scheduling sliding mode control for three-dimensional trajectory tracking of robotic airships, Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 2015, 229, (6), pp 529540.Google Scholar
Yang, Y. and Yan, Y.Neural network approximation-based nonsingular terminal sliding mode control for trajectory tracking of robotic airships, Aerospace Science and Technology, 2016, 54, pp 192197.CrossRefGoogle Scholar
Yi, S., Wang, J. and Li, B.Composite backstepping control with finite-time convergence, Optik, 2017, 142, pp 260272.CrossRefGoogle Scholar
Yu, L. and Fu, M.A robust finite-time output feedback control scheme for marine surface vehicles formation, IEEE Access, 2018, 6, pp 4129141301.CrossRefGoogle Scholar
Zhang, G. and Zhang, X.Concise robust adaptive path-following control of underactuated ships using DSC and MLP, IEEE Journal of Oceanic Engineering, 2013, 39, (4), pp 685694.CrossRefGoogle Scholar
Zhang, J.X. and Yang, G.H.Fuzzy adaptive output feedback control of uncertain nonlinear systems with prescribed performance, IEEE Transactions on Cybernetics, 2017, 48, (5), pp 13421354.CrossRefGoogle ScholarPubMed
Zhang, J., Yu, S. and Yan, Y.Fixed-time output feedback trajectory tracking control of marine surface vessels subject to unknown external disturbances and uncertainties, ISA Transactions, 2019, 93, pp 145155.CrossRefGoogle ScholarPubMed
Zhao, K., Song, Y., Ma, T. and He, L.Prescribed performance control of uncertain Euler–Lagrange systems subject to full-state constraints, IEEE Transactions on Neural Networks and Learning Systems, 2017, 29, (8), pp 34783489.Google ScholarPubMed
Zheng, Z. and Feroskhan, M.Path following of a surface vessel with prescribed performance in the presence of input saturation and external disturbances, IEEE/ASME Transactions on Mechatronics, 2017, 22, (6), pp 25642575.CrossRefGoogle Scholar
Zheng, Z., Feroskhan, M. and Sun, L.Adaptive fixed-time trajectory tracking control of a stratospheric airship, ISA Transactions, 2018, 76, pp 134144.CrossRefGoogle ScholarPubMed
Zheng, Z., Guan, Z., Ma, Y. and Zhu, B.Constrained path-following control for an airship with uncertainties, Engineering Applications of Artificial Intelligence, 2019, 85, pp 295306.CrossRefGoogle Scholar
Zheng, Z., Huang, Y., Xie, L. and Zhu, B.Adaptive trajectory tracking control of a fully actuated surface vessel with asymmetrically constrained input and output, IEEE Transactions on Control Systems Technology, 2017, 26, (5), pp 18511859.CrossRefGoogle Scholar
Zheng, Z., Huo, W. and Wu, Z.Trajectory tracking control for underactuated stratospheric airship, Advances in Space Research, 2012, 50, (7), pp 906917.CrossRefGoogle Scholar
Zheng, Z., Sun, L. and Xie, L.Error-constrained LOS path following of a surface vessel with actuator saturation and faults, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017, 48, (10), pp 17941805.CrossRefGoogle Scholar
Zhou, S. and Song, Y.Prescribed Performance Neuroadaptive Fault-Tolerant Compensation for MIMO Nonlinear Systems Under Extreme Actuator Failures, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019.CrossRefGoogle Scholar
Zhu, E., Pang, J., Sun, N., Gao, H., Sun, Q. and Chen, Z.Airship horizontal trajectory tracking control based on Active Disturbance Rejection Control (ADRC), Nonlinear Dynamics, 2014, 75, (4), pp 725734.CrossRefGoogle Scholar