Document Type : Research Articles
Authors
1 Department of Electrical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
2 Ferdowsi University of Mashhad
3 Electrical department, Faculty of Engineering, Ferdowsi University of Mashhad
4 Department of Electrical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
Abstract
The robust adaptive leader-follower formation control of uncertain unmanned surface vehicles (USVs) subject to stochastic environmental loads is investigated in this paper. The stochastic additive noises are included in the kinematics which stands for the un-modeled dynamics and uncertainty. The disturbances induced by waves, wind and ocean currents in the kinetics are also separated into deterministic and stochastic components. A comprehensive model including kinematics and kinetics of each USV agent is then derived as stochastic differential equations including standard Wiener processes. Thus, the problem formulation is much more challenging and practical since both the exogenous disturbances and kinematics states are defined by stochastic differential equations. In order to guarantee that all the tracking errors converge to a ball centered at the origin in probability, quartic Lyapunov functions synthesis, dynamic surface control (DSC) technique, the projection algorithm, and neural networks (NNs) are employed. Finally, the simulation experiments quantify the effectiveness of proposed approach.
Keywords
- Dynamic Surface Control (DSC)
- Formation Control
- Robust Adaptive Control
- Stochastic Nonlinear Systems
- Unmanned Surface Vehicles (USVs)
Main Subjects
adaptive control design. Wiley New York, 1995.
Mar. 2015, doi: 10.1016/j.automatica.2014.10.022.
Consensus of Multi-Agent Systems: A Brief Survey,” IEEE
Trans. Ind. Electron., vol. 64, no. 6, pp. 4972–4983, Jun.
2017, doi: 10.1109/TIE.2016.2636810.
control with prescribed performance guarantees for USVs,”
IEEE Trans. Ind. Electron., vol. 65, no. 5, pp. 4237–4246,
2017.
formation control of USVs with prescribed performance and
collision avoidance,” IEEE Trans. Ind. Inform., vol. 15, no.
1, pp. 572–581, 2018.
control for autonomous surface vessels with LOS range and
angle constraints,” Automatica, vol. 68, pp. 228–236, 2016.
Springer Science & Business Media, 2013.
of nonlinear systems,” IEEE Trans. Autom. Control, vol. 28,
no. 1, pp. 24–31, Jan. 1983, doi:
10.1109/TAC.1983.1103137.
Business Media, 2013.
Control Based on Backstepping Method for Uncertain
Robotic Manipulators Including Motor Dynamics,” Int. J.
Ind. Electron. Control Optim., vol. 4, no. 1, pp. 13–22, Jan.2021, doi: 10.22111/ieco.2020.31792.1213.
“Dynamic surface control for a class of nonlinear systems,”
IEEE Trans. Autom. Control, vol. 45, no. 10, pp. 1893–1899,
Oct. 2000, doi: 10.1109/TAC.2000.880994.
Output Feedback Control for Nonlinear Full States
Constrained Systems,” IEEE Access, vol. 8, pp. 131590–
131600, 2020, doi: 10.1109/ACCESS.2020.3010027.
Surface Control for High-order Nonlinear System with
Output Constraints,” Int. J. Control Autom. Syst., vol. 19, no.
1, pp. 112–123, Jan. 2021, doi: 10.1007/s12555-019-0986-4.
feedback dynamic surface control design for stochastic
large-scale nonlinear systems with unknown dead zone,”
Neurocomputing, vol. 175, pp. 55–64, 2016.
dynamic surface control for a class of non-strict-feedback
stochastic nonlinear systems,” Int. J. Syst. Sci., vol. 47, no. 1,
pp. 194–208, Jan. 2016, doi:
10.1080/00207721.2015.1043364.
control for full state constrained stochastic nonlinear systems
with unmodeled dynamics,” J. Frankl. Inst., vol. 356, no. 1,
pp. 129–146, Jan. 2019, doi: 10.1016/j.jfranklin.2018.10.011.
Fuzzy Control of Stochastic Nonlinear Systems,” IEEE
Trans. Cybern., vol. 50, no. 6, pp. 2617–2626, Jun. 2020,
doi: 10.1109/TCYB.2019.2925573.
Neural Output-Feedback Decentralized Control for LargeScale Nonlinear Systems With Stochastic Disturbances,”
IEEE Trans. Neural Netw. Learn. Syst., vol. 31, no. 3, pp.
972–983, Mar. 2020, doi: 10.1109/TNNLS.2019.2912082.
Adaptive Fuzzy Control for Stochastic Nonlinear Systems
with Unmeasured States and Unknown Backlash-Like
Hysteresis,” IEEE Trans. Fuzzy Syst., pp. 1–1, 2020, doi:
10.1109/TFUZZ.2020.2973950.
Obstacle Avoidance for a Class of Stochastic Multiagent
Systems,” IEEE Trans. Ind. Electron., vol. 65, no. 7, pp.
5847–5855, Jul. 2018, doi: 10.1109/TIE.2017.2782229.
systems via dynamic output feedback control,” Automatica,
vol. 107, pp. 418–424, Sep. 2019, doi:
10.1016/j.automatica.2019.06.006.
observer-based integral sliding mode control for an
underwater robot with unknown disturbances and uncertain
nonlinearities,” IEEE Trans. Ind. Electron., vol. 64, no. 8, pp.
6785–6795, 2017.
structures. Cambridge University Press, 2013.
stochastic environmental loads in three dimensional space,”
Ocean Eng., vol. 99, pp. 34–43, 2015.
underactuated ships under stochastic disturbances,” Ocean
Eng., vol. 111, pp. 267–278, Jan. 2016, doi:
10.1016/j.oceaneng.2015.10.038.
Autom. Control, vol. 37, no. 6, pp. 729–740, 1992.
Calculus. Springer, 2014.
stochastic nonlinear systems driven by noise of unknown
covariance,” IEEE Trans. Autom. Control, vol. 46, no. 8, pp.
1237–1253, 2001.
maneuvering, with experiments, for a model ship in a marine
control laboratory,” Automatica, vol. 41, no. 2, pp. 289–298,
Feb. 2005, doi: 10.1016/j.automatica.2004.10.006.
)