Hostname: page-component-848d4c4894-nr4z6 Total loading time: 0 Render date: 2024-05-15T09:18:54.925Z Has data issue: false hasContentIssue false

Terminal sliding-mode disturbance observer-based finite-time adaptive-neural formation control of autonomous surface vessels under output constraints

Published online by Cambridge University Press:  12 September 2022

Amir Naderolasli
Affiliation:
Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
Khoshnam Shojaei*
Affiliation:
Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran Digital Processing and Machine Vision Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran
Abbas Chatraei
Affiliation:
Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran Digital Processing and Machine Vision Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran
*
*Corresponding author. E-mail: khoshnam.shojaee@gmail.com

Abstract

This paper proposes a tracking controller for the formation construction of multiple autonomous surface vessels (ASVs) in the presence of model uncertainties and external disturbances with output constraints. To design a formation control system, the leader-following strategy is adopted for each ASV. A symmetric barrier Lyapunov function (BLF), which advances to infinity when its arguments reach a finite limit, is applied to prevent the state variables from violating constraints. An adaptive-neural technique is employed to compensate uncertain parameters and unmodeled dynamics. To overcome the explosion of differentiation term problem, a first-order filter is proposed to realize the derivative of virtual variables in the dynamic surface control (DSC). To estimate the leader velocity in finite time, a high-gain observer is effectively employed. This approach is adopted to reveal all signals of the closed-loop system which are bounded, and the formation tracking errors are semi-globally finite-time uniformly bounded. The computer simulation results demonstrate the efficacy of this newly proposed formation controller for the autonomous surface vessels.

Type
Research Article
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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

Toda, Y. and Kubota, N., “Self-localization based on multiresolution map for remote control of multiple mobile robots,” IEEE Trans. Ind. Inform. 9(3), 17721781 (2013).CrossRefGoogle Scholar
Yousuf, B. M., Khan, A. S. and Noor, A., “Multi-agent tracking of non-holonomic mobile robots via non-singular terminal sliding mode control,” Robotica 38(11), 19842000 (2020).CrossRefGoogle Scholar
Dai, Y. and Lee, S. G., “Leader–follower formation control of underactuated autonomous underwater vehicles,” Int. J. Control Autom. 10(2), 350361 (2012).CrossRefGoogle Scholar
Huda, M. N., Yu, H. and Cang, S., “Behavior-based control approach for the trajectory tracking of an underactuated planar capsule robot,” IET Control Theory Appl. 9(2), 163175 (2014).CrossRefGoogle Scholar
Nakhaeinia, D. and Karasfi, B., “A behavior-based approach for collision avoidance of mobile robots in unknown and dynamic environments,” J. Intell. Fuzzy Syst. 24(2), 299311 (2013).CrossRefGoogle Scholar
Ren, W. and Beard, R. W., “Formation feedback control for multiple spacecraft via virtual structures,” IEE Proc. Control Theory Appl. 151(3), 357368 (2004).CrossRefGoogle Scholar
Lee, Y. H., Kim, S. G., Park, T. Y., Ji, S. H., Moon, Y. S. and Cho, Y. J., “Virtual target tracking of mobile robot and its application to formation control,” Int. J. Control Autom. Syst. 12(2), 390398 (2014).CrossRefGoogle Scholar
Peng, Z., Wen, G., Rahmani, A. and Yu, Y., “Leader–follower formation control of nonholonomic mobile robots based on a bioinspired neurodynamic based approach,” Robot. Auton. Syst. 61(9), 988996 (2013).CrossRefGoogle Scholar
Shojaei, K., “Neural network formation control of a team of tractor–trailer systems,” Robotica 36(1), 3956 (2018).CrossRefGoogle Scholar
Shojaei, K., “Neural adaptive PID formation control of car-like mobile robots without velocity measurements,” Adv. Robot. 31(18), 947964 (2017).CrossRefGoogle Scholar
Shojaei, K., “Neural network formation control of underactuated autonomous underwater vehicles with saturating actuators,” Neurocomputing 194(6), 372384 (2016).CrossRefGoogle Scholar
Peng, Z., Wang, D., Chen, Z., Hu, X. and Lan, W., “Adaptive dynamic surface control for formations of autonomous surface vehicles with uncertain dynamics,” IEEE Trans. Control Syst. Technol. 21(2), 513520 (2012).CrossRefGoogle Scholar
Dai, S. L., He, S., Lin, H. and Wang, C., “Platoon formation control with prescribed performance guarantees for USVs,” IEEE Trans. Ind. Electron. 65(5), 42374246 (2017).CrossRefGoogle Scholar
Wei, C., Luo, J., Dai, H. and Duan, G., “Learning-based adaptive attitude control of spacecraft formation with guaranteed prescribed performance,” IEEE Trans. Cybern. 99, 113 (2018).Google Scholar
Niu, B. and Zhao, J., “Barrier lyapunov functions for the output tracking control of constrained nonlinear switched systems,” Syst. Control Lett. 62(10), 963971 (2013).CrossRefGoogle Scholar
Tee, K. P. and Ge, S. S., “Control of nonlinear systems with partial state constraints using a barrier lyapunov function,” Int. J. Control 84(12), 20082023 (2011).CrossRefGoogle Scholar
Dunbar, W. B. and Murray, R. M., “Distributed receding horizon control for multi-vehicle formation stabilization,” Automatica 42(4), 549558 (2006).CrossRefGoogle Scholar
Jin, X., “Fault tolerant finite-time leader–follower formation control for autonomous surface vessels with LOS range and angle constraints,” Automatica 68(2), 228236 (2016).CrossRefGoogle Scholar
He, W., Yin, Z. and Sun, C., “Adaptive neural network control of a marine vessel with constraints using the asymmetric barrier lyapunov function,” IEEE Trans. Cybern. 47(7), 16411651 (2016).CrossRefGoogle Scholar
Zhang, C., Yan, Y., Narayan, A. and Yu, H., “Practically oriented finite-time control design and implementation: application to a series elastic actuator,” IEEE Trans. Ind. Electron. 65(5), 41664176 (2017).CrossRefGoogle Scholar
Zhang, C., Yan, Y., Narayan, A. and Yu, H., “Neural network disturbance observer-based distributed finite-time formation tracking control for multiple unmanned helicopters,” ISA Trans. 73(7), 208226 (2018).Google Scholar
Huang, C., Zhang, X. and Zhang, G., “Improved decentralized finite-time formation control of underactuated USVs via a novel disturbance observer,” Ocean Eng. 174, 117124 (2019).CrossRefGoogle Scholar
Homayounzade, M. and Khademhosseini, A., “Disturbance observer-based trajectory following control of robot manipulators,” Int. J. Control Autom. 17(1), 203211 (2019).CrossRefGoogle Scholar
Zou, A., de Ruiter, A. H. and Kumar, K. D., “Disturbance observer-based attitude control for spacecraft with input MRS,” IEEE Trans. Aerosp. Electron. Syst. 55(1), 384396 (2018).CrossRefGoogle Scholar
Mohammadi, A., Tavakoli, M., Marquez, H. J. and Hashemzadeh, F., “Nonlinear disturbance observer design for robotic manipulators,” Control Eng. Pract. 21(3), 253267 (2013).CrossRefGoogle Scholar
Do, K. D. and Pan, J., “Nonlinear control of an active heave compensation system,” Ocean Eng. 35(5), 558571 (2008).CrossRefGoogle Scholar
Park, B. S. and Pan, J., “Adaptive formation control of underactuated autonomous underwater vehicles,” Ocean Eng. 96(5), 17 (2015).CrossRefGoogle Scholar
Zhang, J., Liu, X., Xia, Y., Zuo, Z. and Wang, Y., “Disturbance observer-based integral sliding-mode control for systems with mismatched disturbances,” IEEE Trans. Ind. Electron. 63(11), 70407048 (2016).CrossRefGoogle Scholar
Zhu, Y., Qiao, J. and Guo, L., “Adaptive sliding mode disturbance observer-based composite control with prescribed performance of space manipulators for target capturing,” IEEE Trans. Ind. Electron. 66(3), 19731983 (2018).CrossRefGoogle Scholar
Chen, H., Ren, H., Gao, Z., Yu, F., Guan, W. and Wang, D., “Disturbance observer-based finite-time control scheme for dynamic positioning of ships subject to thruster faults,” Int. J. Robust. Nonlin. Cont. 31(13), 62556271 (2021).CrossRefGoogle Scholar
Hu, X., Wei, X., Kao, Y. and Han, J., “Robust synchronization for under-actuated vessels based on disturbance observer,” IEEE Trans. Intell. Transp. Syst. 23(6), 54705479 (2021). doi: 10.1016/j.isatra.2020.12.044 CrossRefGoogle Scholar
Ghommam, J. and Chemori, A., “Adaptive RBFNN finite-time control of normal forms for underactuated mechanical systems,” Nonlinear Dyn. 90(1), 301315 (2017).CrossRefGoogle Scholar
Huang, C., Zhang, X. and Zhang, G., “Adaptive neural finite-time formation control for multiple underactuated vessels with actuator faults,” Ocean Eng. 222, 108556 (2021).CrossRefGoogle Scholar
Peng, Z., Wang, J., Wang, D. and Han, Q. L., “An overview of recent advances in coordinated control of multiple autonomous surface vehicles,” IEEE Trans. Ind. Inform. 17(2), 732745 (2020).CrossRefGoogle Scholar
Gu, N., Wang, D., Peng, Z., Wang, J. and Han, Q. L., “Advances in line-of-sight guidance for path following of autonomous marine vehicles: an overview,” IEEE Trans. Syst. Man Cybern. Syst., 117 (2022). https://ieeexplore.ieee.org/document/9750396 CrossRefGoogle Scholar
Gu, N., Wang, D., Peng, Z., Wang, J. and Han, Q. L., “Disturbance observers and extended state observers for marine vehicles: A survey” control eng,” Cont. Eng. Pract. 123, 105158 (2022).CrossRefGoogle Scholar
Yu, J., Dong, X., Li, Q., Lu, J. and Ren, Z., “Adaptive practical optimal time-varying formation tracking control for disturbed High-Order Multi-Agent systems,” IEEE Trans. Circ. Syst. I 69(6), 25672578 (2022). doi: 10.1109/TCSI.2022.3151464.Google Scholar
Du, H., Zhu, W., Wen, G., Duan, Z. and Lu, J., “Distributed formation control of multiple quadrotor aircraft based on nonsmooth consensus algorithms,” IEEE Trans. Cybern. 49(1), 342353 (2017).CrossRefGoogle ScholarPubMed
Ren, W., “Consensus Tracking Under Directed Interaction Topologies: Algorithms and Experiments,” In: 2008 American Control Conference (IEEE, June 2008) pp. 742747.Google Scholar
Gu, N., Wang, D., Peng, Z. and Wang, J., “Safety-critical containment maneuvering of underactuated autonomous surface vehicles based on neurodynamic optimization with control barrier functions,” IEEE Trans. Neur. Net. Lear., 1-14 (2021). doi: 10.1109/TNNLS.2021.3110014.CrossRefGoogle ScholarPubMed
Tee, K. P., Ren, B. and S. S, “Ge “Control of nonlinear systems with time-varying output constraints,” Automatica 47(11), 25112516 (2011).CrossRefGoogle Scholar
, K. P. Tee, S. S., Ge and E. H., Tay, “Barrier Lyapunov functions for the control of output-constrained nonlinear systems,” Automatica 45(4), 918927 (2009).CrossRefGoogle Scholar
Xiao, B., Yang, X. and Huo, X., “A novel disturbance estimation scheme for formation control of ocean surface vessels,” IEEE Trans. Ind. Electron. 64(6), 49945003 (2016).CrossRefGoogle Scholar
Sarrafan, N. and K. Shojaei, “High-gain observer-based neural adaptive feedback linearizing control of a team of wheeled mobile robots,” Robotica 38(1), 6987 (2020).CrossRefGoogle Scholar
Tee, K. P. and S. S. Ge, “Control of fully actuated ocean surface vessels using a class of feedforward approximators,” IEEE Trans. Contr. Syst. Trans. 14(4), 750756 (2006).CrossRefGoogle Scholar
Khalil, H. K., “Cascade high-gain observers in output feedback control,” Automatica 80, 110118 (2017).CrossRefGoogle Scholar