Hostname: page-component-848d4c4894-m9kch Total loading time: 0 Render date: 2024-04-30T16:46:27.048Z Has data issue: false hasContentIssue false
  • English
  • Français

Neural network-based robust adaptive super-twisting sliding mode fault-tolerant control for a class of tilt tri-rotor UAVs with unmodeled dynamics

Published online by Cambridge University Press:  08 April 2024

L. Chao ,
Y. Bai ,
Z. Wang Open the ORCID record for Z. Wang [Opens in a new window]  and
Y. Yin Open the ORCID record for Y. Yin [Opens in a new window]
L. Chao
Affiliation:
Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an, China China Academy of Launch Vehicle Technology, Beijing, China
Y. Bai
Affiliation:
China Academy of Launch Vehicle Technology, Beijing, China
Z. Wang*
Affiliation:
Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an, China Research Center for Unmanned System Strategy Development, Northwestern Polytechnical University, Xi’an, China Northwest Institute of Mechanical and Electrical Engineering, Xianyang, China National Key Laboratory of Aerospace Flight Dynamics, Northwestern Polytechnical University, Xi’an, China
Y. Yin
Affiliation:
National Key Laboratory of Aerospace Flight Dynamics, Northwestern Polytechnical University, Xi’an, China
*
Corresponding author: Z. Wang; Email: wz_nwpu@126.com
Get access Rights & Permissions [Opens in a new window]

Abstract

Aiming at alleviating the adverse influence of coupling unmodeled dynamics, actuator faults and external disturbances in the attitude tracking control system of tilt tri-rotor unmanned aerial vehicle (UAVs), a neural network (NN)-based robust adaptive super-twisting sliding mode fault-tolerant control scheme is designed in this paper. Firstly, in order to suppress the unmodeled dynamics coupled with the system states, a dynamic auxiliary signal, exponentially input-to-state practically stability and some special mathematical tools are used. Secondly, benefiting from adaptive control and super-twisting sliding mode control (STSMC), the influence of the unexpected chattering phenomenon of sliding mode control (SMC) and the unknown system parameters can be handled well. Moreover, NNs are employed to estimate and compensate some unknown nonlinear terms decomposed from the system model. Based on a decomposed quadratic Lyapunov function, both the bounded convergence of all signals of the closed-loop system and the stability of the system are proved. Numerical simulations are conducted to demonstrate the effectiveness of the proposed control method for the tilt tri-rotor UAVs.

Keywords

tilt tri-rotor UAVsSTSMCunmodeled dynamicsactuator faultsneural networks
Type
Research Article
Information
The Aeronautical Journal , First View , pp. 1 - 20
Check for updates
Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of Royal Aeronautical Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Hegde, N.T., George, V.I., Nayak, C.G. and Kumar, K. Design, dynamic modelling and control of tilt-rotor UAVs: a review, Int. J. Intell. Unmanned Syst., 2019, 8, (3), pp 143–161.CrossRefGoogle Scholar
Mohamed, M.K. and Lanzon, A. Design and control of novel tri-rotor UAV, Proceedings of 2012 UKACC International Conference on Control, IEEE, 2012, pp 304–309.CrossRefGoogle Scholar
Escareno, J., Sanchez, A., Garcia, O. and Lozano, R. Triple tilting rotor mini-UAV: modeling and embedded control of the attitude, 2008 American Control Conference, IEEE, 2008, pp 3476–3481.CrossRefGoogle Scholar
Hao, W., Xian, B. and Xie, T. Fault-tolerant position tracking control design for a tilt tri-rotor unmanned aerial vehicle, IEEE Trans. Indus. Electron., 2021, 69, (1), pp 604–612.CrossRefGoogle Scholar
Utkin, V.I. Sliding Modes in Control and Optimization, Springer Science & Business Media, 2013.Google Scholar
Alwi, H., Edwards, C. and Tan, C.P. Fault Detection and Fault-Tolerant Control Using Sliding Modes, Springer, 2011, London.CrossRefGoogle Scholar
Shtessel, Y., Edwards, C., Fridman, L. and Levant, A. Sliding Mode Control and Observation, Springer New York, 2014, New York.CrossRefGoogle Scholar
Tao, G. Multivariable adaptive control: a survey, Automatica, 2014, 50, (11), pp 2737–2764.CrossRefGoogle Scholar
Lavretsky, E. and Wise, K.A. Robust Adaptive Control, in Robust and Adaptive Control, Springer, 2013, London, pp 317–353.CrossRefGoogle Scholar
Wang, Z. and Pan, Y. Robust adaptive fault tolerant control for a class of nonlinear systems with dynamic uncertainties, Optik, 2017, 131, pp 941–952.CrossRefGoogle Scholar
Shtessel, Y., Fridman, L. and Plestan, F. Adaptive sliding mode control and observation, Int. J. Control, 2016, 89, (9), pp 1743–1746.CrossRefGoogle Scholar
Liu, K. and Wang, R. Antisaturation adaptive fixed-time sliding mode controller design to achieve faster convergence rate and its application, IEEE Trans. Circ. Syst. II Express Briefs, 2022, 69, (8), pp 3555–3559.Google Scholar
Liu, K., Yang, P., Wang, R., Jiao, L., Li, T. and Zhang, J. Observer-based adaptive fuzzy finite-time attitude control for quadrotor UAVs, IEEE Trans. Aerospace Electron. Syst., 2023, 59, (6), pp 8637–8654.CrossRefGoogle Scholar
Liu, K., Yang, P., Jiao, L., Wang, R., Yuan, Z. and Dong, S. Antisaturation fixed-time attitude tracking control based low-computation learning for uncertain quadrotor UAVs with external disturbances, Aerospace Sci. Technol., 2023, 142, p 108668.CrossRefGoogle Scholar
Levant, A. Sliding order and sliding accuracy in sliding mode control, Int. J. Control, 1993, 58, (6), pp 1247–1263.CrossRefGoogle Scholar
Levant, A. Robust exact differentiation via sliding mode technique, Automatica, 1998, 34, (3), pp 379–384.CrossRefGoogle Scholar
Tian, B., Cui, J., Lu, H., Liu, L. and Zong, Q. Attitude control of UAVs based on event-triggered supertwisting algorithm, IEEE Trans. Indus. Inf., 2020, 17, (2), pp 1029–1038.CrossRefGoogle Scholar
Ali, S.U., Samar, R., Shah, M.Z., Bhatti, A.I., Munawar, K. and Al-Sggaf, U.M. Lateral guidance and control of UAVs using second-order sliding modes, Aerospace Sci. Technol., 2016, 49, pp 88–100.CrossRefGoogle Scholar
Zhai, J. and Li, Z. Fast-exponential sliding mode control of robotic manipulator with super-twisting method, IEEE Trans. Circ. Syst. II Express Briefs, 2021, 69, (2), pp 489–493.Google Scholar
Ahmed, S., Ahmed, A., Mansoor, I., Junejo, F. and Saeed, A. Output feedback adaptive fractional-order super-twisting sliding mode control of robotic manipulator, Iranian J. Sci. Technol. Trans. Electr. Eng., 2021, 45, (1), pp 335–347.CrossRefGoogle Scholar
Salgado, I., Kamal, S., Bandyopadhyay, B., Chairez, I. and Fridman, L. Control of discrete time systems based on recurrent super-twisting-like algorithm, ISA Trans., 2016, 64, pp 47–55.CrossRefGoogle ScholarPubMed
Shtessel, Y., Taleb, M. and Plestan, F. A novel adaptive-gain supertwisting sliding mode controller: Methodology and application, Automatica, 2012, 48, (5), pp 759–769.CrossRefGoogle Scholar
Wang, Z., Yuan, J. and Pan, Y. Adaptive second-order sliding mode control: a unified method, Trans. Inst. Meas. Control, 2018, 40, (6), pp 1927–1935.CrossRefGoogle Scholar
Gonzalez, T., Moreno, J.A. and Fridman, L. Variable gain super-twisting sliding mode control, IEEE Trans. Autom. Control, 2011, 57, (8), pp 2100–2105.CrossRefGoogle Scholar
Wang, Y., Ma, H. and Wu, W. Robust tracking control of the Euler–Lagrange system based on barrier Lyapunov function and self-structuring neural networks, Comput. Intell. Neurosci., 2021, 2021, pp 1–13.Google ScholarPubMed
Zhao, L., Li, J., Li, H., and Liu, B. Double-loop tracking control for a wheeled mobile robot with unmodeled dynamics along right angle roads, ISA Trans., 2023, 136, pp 525–534.CrossRefGoogle ScholarPubMed
Wang, Z. and Xian, B. Finite time convergence control design of the tilt tri-rotor unmanned aerial vehicle, Control Theory Appl., 2019, 36, (9), pp 1442–1452.Google Scholar
Ning, X., Zhang, Y. and Wang, Z. Robust adaptive control for a class of TS fuzzy nonlinear systems with discontinuous multiple uncertainties and abruptly changing actuator faults, Complexity, 2020, 2020, pp 1–16.Google Scholar
Zhu, Z., Xia, Y. and Fu, M. Attitude stabilization of rigid spacecraft with finite-time convergence, Int. J. Robust Nonlinear Control, 2011, 21, (6), pp 686–702.CrossRefGoogle Scholar
Wang, Z., Yuan, Y. and Yang, H. Adaptive fuzzy tracking control for strict-feedback Markov jumping nonlinear systems with actuator failures and unmodeled dynamics, IEEE Trans. Cybern., 2018, 50, (1), pp 126–139.CrossRefGoogle ScholarPubMed
Liang, H., Liu, G., Zhang, H., and Huang, T. Neural-network-based event-triggered adaptive control of nonaffine nonlinear multiagent systems with dynamic uncertainties, IEEE Trans. Neural Networks Learn. Syst., 2020, 32, (5), pp 2239–2250.CrossRefGoogle Scholar
Ma, L., Huo, X., Zhao, X., and Zong, G.D. Observer-based adaptive neural tracking control for output-constrained switched MIMO nonstrict-feedback nonlinear systems with unknown dead zone, Nonlinear Dyn., 2020, 99, (2), pp 1019–1036.CrossRefGoogle Scholar
Wang, W., Pan, Y. and Liang, H. Adaptive neural finite-time containment control for nonlower triangular nonlinear multi-agent systems with dynamics uncertainties, Neurocomputing, 2020, 391, pp 157–166.CrossRefGoogle Scholar
Liu, Q., Li, D., Ge, S.S., Ji, R., Ouyang, Z. and Tee, K.P. Adaptive bias RBF neural network control for a robotic manipulator, Neurocomputing, 2021, 447, pp 213–223.CrossRefGoogle Scholar
Jiang, Z.P. and Praly, L. Design of robust adaptive controllers for nonlinear systems with dynamic uncertainties, Automatica, 1998, 34, (7), pp 825–840.CrossRefGoogle Scholar
Trinh, H. Stability analysis and control of two-dimensional fuzzy systems with directional time-varying delays, IEEE Trans. Fuzzy Syst., 2017, 26, (3), pp 1550–1564.Google Scholar
Liu, K., Wang, R., Zheng, S., Dong, S. and Sun, G. Fixed-time disturbance observer-based robust fault-tolerant tracking control for uncertain quadrotor UAV subject to input delay, Nonlinear Dyn., 2022, 107, (3), pp 2363–2390.CrossRefGoogle Scholar