Fixed-time Fault Tolerant Control Based on Neural Network and Adaptive Sliding Mode Controller of Autonomous Underwater Vehicle with Actuator Saturation

Document Type : Original Article

Authors

Shahid Beheshti University - Faculty of Mechanics and Energy

Abstract

The complexity of the sea environment, external disturbances and uncertainties in the system, as well as the failure and saturation of the actuators, are effective factors in the control of underwater vehicles. In this note, to deal with the mentioned problems, a robust and adaptive controller is proposed by combining the fast terminal dynamic sliding mode controller and the radial basis neural network. In the proposed approach, the problem of actuator saturation is considered and its stability is proved using Lyapunov theory. Fix-time convergence, estimation and dealing with external disturbances and uncertainties, active dealing with actuator fault, elimination of chattering phenomenon, and non-saturation of the actuator are the advantages of the proposed controller. The designed controller is applied on an autonomous underwater vehicle and the controller parameters are optimized to achieve the least error in tracking the reference trajectories as well as the shortest convergence time. To evaluate the performance of the proposed controller, it has been compared with a passive fault control method and PID controller, which has shown the superiority of the proposed control method in tracking the desired trajectories, the amount of control effort, dealing with actuators faults and external disturbances, as well as convergence in less time.

Keywords

Main Subjects

References

  1. L. Seto, and A. Z. Bashir, “Fault tolerance considerations for long endurance AUVs”, Annual Reliability and Maintainability Symposium, pp. 1-6, 2017. https://doi.org/10.1109/RAM.2017.7889661
  2. Kadiyam, A. Parashar, S. Mohan, and D. Deshmukh, “Actuator fault-tolerant control study of an underwater robot with four rotatable thrusters”, Ocean Engineering, vol. 197, pp. 106929, 2020. https://doi.org/10.1016/j.oceaneng.2020.106929
  3. K. Alekseev, V. V. Kostenko, and A. Y. Shumsky, “Use of identification and fault diagnostic methods for underwater robotics”, Proceedings of OCEANS, vol. 2, pp. 489-494, 1994. https://doi.org/10.1109/OCEANS.1994.364093
  4. Navi, M. Davoodi, and N. Meskin, “Sensor fault detection and isolation of an autonomous underwater vehicle using partial Kernel PCA”, IEEE Conference on Prognostics and Health Management (PHM), pp. 1-9, 2015. https://doi.org/10.1109/ICPHM.2015.7245022
  5. Omerdic, G.N. Roberts and P. Ridao, , “Fault Detection and Accommodation for ROVs”, IFAC Proceedings Volumes, vol. 36, pp. 127-132, 2003. https://doi.org/10.1016/S1474-6670(17)37795-9
  6. Mokhtari, M. Taghizadeh, and P. Ghaf‑Ghanbari, “Adaptive second‑order sliding model‑based fault‑tolerant control of a lower‑limb exoskeleton subject to tracking the desired trajectories augmented by CPG algorithm”, Journal of the Brazilian Society of Mechanical Sciences and Engineering, vol. 44, no. 423, 2022. https://doi.org/10.1007/s40430-022-03694-6
  7. Yang, M. Zhang, Y. Wang, and J. Wu, “Study on simultaneous fault tolerant control of AUV thrusters”. IEEE International Conference on Automation and Logistics, pp. 105-110, 2008.https://doi.org/10.1109/ICAL.2008.4636129
  8. Mokhtari, M. Taghizadeh, and P. Ghaf-Ghanbari, “Fault tolerant control based on backstepping nonsingular terminal integral sliding mode and impedance control for a lower limb exoskeleton”, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science (PIC), vol. 236, no. 6, pp. 2698-2713, 2022. https://doi.org/10.1177/09544062211035792
  9. K. Podder, G. Antonelli, and N. Sarkar, “Fault tolerant control of an autonomous underwater vehicle under thruster redundancy: simulations and experiments”. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings, 2000. https://doi.org/10.1109/ROBOT.2000.844770
  10. Liu, and D. Zhu, “Fault-tolerant control of unmanned underwater vehicles with continuous Faults: simulations and experiments”, International Journal of Advanced Robotic Systems, vol. 6, no. 4, pp. 301-308, 2009. https://doi.org/10.5772/7244
  11. C. Karras, P. Marantos, C. P. Bechlioulis, and K. J. Kyriakopoulos, “Unsupervised Online System Identification for Underwater Robotic Vehicles”, IEEE Journal of Oceanic Engineering, vol. 44, no. 3, pp. 642-663, 2019. https://doi.org/10.1109/JOE.2018.2827678
  12. Zhu, B. Huang, B. Zhou, Y. Su, and E. Zhang, “Adaptive model-parameter-free fault-tolerant trajectory tracking control for autonomous underwater vehicles”, ISA Transactions, vol. 144, pp. 57-71, 2021. https://doi.org/10.1016/j.isatra.2020.12.059
  13. Ahmed Amin, Kh. Mahmood Hassan, “A review of fault tolerant control systems: Advancements and applications”. Measurement, vol. 143, pp. 58-68, 2019. https://doi.org/10.1016/j.measurement.2019.04.083
  14. Shen, Y. Shi, and B. Buckham, “Trajectory tracking control of an autonomous underwater vehicle using Lyapunov-based model predictive control”, IEEE, vol. 65, no. 7, pp. 5796–5805, 2018. https://doi.org/10.1109/TIE.2017.2779442
  15. F. Vahidifar, “An Improved Shooting Method for a Class of Switching Optimal Control Problems”,Journal Of Applied and Computational Sciences in Mechanics, 31, no. 1, pp. 123-138, 2020. (In Persian) https://doi.org/10.22067/fum-mech.v31i1.77967
  16. Hu, H. Yan, H. Zhang, M. Wang, and L. Zeng, “Robust Adaptive Fixed-Time Sliding-Mode Control for Uncertain Robotic Systems with Input Saturation”, IEEE Transactions on Cybernetics, vol. 53, no. 4, pp. 2636-2646, 2023. https://doi.org/10.1109/TCYB.2022.3164739
  17. M. Mosalsal, and M. Khodabandeh, “Variable-Pitch Control of a Quadrotor Using Feedback Linearization Controller and Direct Adaptive Feedback Linearization Controller”, Journal of Applied and Computational Sciences in Mechanics, 31, no. 2, pp. 67-90, 2020. (In Persian) https://doi.org/10.22067/fum-mech.v31i2.84510
  18. Kalani, and A. Akbarzadeh, “Application of Reinforcement Learning for Navigation of a Planar Snake Robot in Serpentine Locomotion”, Journal of Applied and Computational Sciences in Mechanics, vol. 26, no. 1, pp. 97-118, 2015. (In Persian) https://doi.org/10.22067/fum-mech.v26i1.20871
  19. Khorshidi, and H. Moeenfard, “Beyond Pull-in Stabilization of a 2-DOF Torsional Micro-Actuator using a Fuzzy Controller”, Journal of Applied and Computational Sciences in Mechanics, vol. 27, no. 2, pp. 99-112, 2016. (In Persian) https://doi.org/10.22067/fum_mech.v27i2.40523
  20. Zarabadipour, and M. Farhangranj, “Robust-Adaptive Sliding Mode Controller Design with Fault tolerance for Active Suspension of Half-car model”, Journal of Applied and Computational Sciences in Mechanics, vol. 34, no. 3, pp. 79-96, 2022. (In Persian) https://doi.org/10.22067/jacsm.2022.71589.1043
  21. Wu, H. Fang, T. Xu, and F. Wan, “Adaptive Neural Fixed-time Sliding Mode Control of Uncertain Robotic Manipulators with Input Saturation and Prescribed Constraints”, Neural Processing Letters, vol. 54, pp. 3829-3849, 2022. https://doi.org/10.1007/s11063-022-10788-8
  22. Mokhtari, M. Taghizadeh, and M. Mazare, “Optimal adaptive super twisting sliding mode control base on zero moment point stability criterion of a lower limb exoskeleton”, Amir Kabir Journal of Mechanical Engineering, vol. 50, no. 4, pp. 525-532, 2020. (In Persian) https://doi.org/10.22060/mej.2019.16292.6321
  23. Mokhtari, M. Taghizadeh, M. Mazare, “Impedance Control Based on Optimal Adaptive High Order Super Twisting Sliding Mode for a 7-DOF Lower Limb Exoskeleton”, Meccanica, vol. 56, pp. 538-548, 2021. https://doi.org/10.1007/s11012-021-01308-4
  24. Mokhtari, M. Taghizadeh, and M. Mazare, “Active fault tolerant control based on adaptive back-stepping nonsingular fast integral terminal sliding mode approach”, Amir Kabir Journal of Mechanical Engineering, vol. 53, no. 6, pp. 3763-3782, 2021. (In Persian) https://doi.org/10.22060/mej.2021.18277.6789
  25. Qiao, and W. Zhang, “Adaptive second-order fast nonsingular terminal sliding mode tracking control for fully actuated autonomous underwater vehicles”, IEEE, vol. 44, no. 2, pp. 363–385, 2019. https://doi.org/10.1109/JOE.2018.2809018
  26. Hu, L. Xi, and Ch. Wang, “Adaptive fault-tolerant attitude tracking control for spacecraft with time-varying inertia uncertainties”, Chinese Journal of Aeronautics, vol. 32, no. 3, pp. 674–687, 2019. https://doi.org/10.1016/j.cja.2018.12.015
  27. Ma, S. S. Ge, and Z. Zheng, "Adaptive NN control of a class of nonlinear systems with asymmetric saturation actuators". IEEE Trans Neural Network Learn System, vol. 26, no. 7, pp. 1532–1538, 2015. https://doi.org/10.1109/TNNLS.2014.2344019
  28. Zhu, and J. Du, “Robust adaptive neural practical fixed-time tracking control for uncertain Euler-Lagrange systems under input saturations”, Neurocomputing, vol. 412, pp. 502–513, 2020. https://doi.org/10.1016/j.neucom.2020.05.057
  29. Jalili, M. Malek- Jafarian, and A. Safavinejad, “Introduction of Harmony Search Algorithm for Aerodynamic Shape Optimization Using the Navier-Stokes Equations”, Journal of Applied and Computational Sciences in Mechanics, vol. 2, no. 2, pp. 81-96, 2013. (In Persian)
  30. T. Hagan, and M. B. Menhaj, “Training feedforward networks with the Marquardt algorithm”, IEEE Trans Neural Netw, vol. 5, no. 6, pp. 989-993, 1994. https://doi.org/10.1109/72.329697
  31. Jin, J. Kim, J. Kim, and T. Seo, “Six-Degree-of-Freedom hovering control of an underwater robotic platform with four tilting thrusters via selective switching control”, IEEE/ASME Transactions on Mechatronics, vol. 20, no. 5, pp. 2370–2378, 2015.
  32. J. Craig, “Introduction to Robotics: Mechanics and Control, Upper Saddle River,” Pearson/Prentice Hall, 2005. https:// doi.org/10.1109/JRA.1987.1087086
  33. Chen, G. Tao, and B. Jiang, “Dynamic surface control using neural networks for a class of uncertain nonlinear systems with input saturation”, IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 9, pp. 2086–2097, 2015. https://doi.org/10.1109/TNNLS.2014.2360933
  34. Zuo, B. Tian, M. Defoort, and Zh. Ding, “Fixed-time consensus tracking for multi-agent systems with high-order integrator dynamics”, IEEE Trans. Autom Control, vol. 63, no. 2, pp. 563–570, 2018. https://doi.org/10.1109/TAC.2017.2729502
  35. Zheng, M. Feroskhan, and L. Sun, “Adaptive fixed-time trajectory tracking control of a stratospheric airship”, ISA Transacion, vol. 76, pp. 134-144, 2018. https://doi.org/10.1016/j.isatra.2018.03.016
  36. Mokhtari, M. Taghizadeh, and M. Mazare, “Robust and Adaptive Control of an Exoskeleton Robot for Tracking Modified Desired Trajectory Based on Zero Moment Point Stability Theory”, Amir Kabir Journal of Mechanical Engineering, vol. 53, no. 12, pp. 5831-5850, 2022. (In Persian) https://doi.org/10.22060/mej.2021.19761.7106
Send comment about this article
CAPTCHA Image

Files

Share

How to cite

Statistics