This paper addresses the critical issue of dynamic performance constraints for unmanned surface vessels (USVs) operating under real maritime conditions. First, to accurately characterize the effect of ocean waves on unmanned vessels, a wave disturbance model is established based on actual ocean wave measurements, which provides more accurate representation of maritime environmental disturbances. Subsequently, an optimized barrier Lyapunov function is designed to constrain the position errors that occur during the USV’s trajectory tracking process. Furthermore, a state-constrained control strategy that incorporates propeller dynamics is developed, which can effectively mitigate oscillations in control inputs. Finally, numerical simulations using SimuNPS software demonstrate that the proposed control strategy exhibits significant advantages over existing methods.
Alvaro-MendozaEGonzalez-GarciaACastanedaH (2023) Novel adaptive law for super-twisting controller: USV tracking control under disturbances. ISA Transactions139: 561–573.
2.
BechlioulisCRovithakisG (2008) Robust adaptive control of feedback linearizable MIMO nonlinear systems with prescribed performance. IEEE Transactions on Automatic Control53(9): 2090–2099.
3.
BelleterDGaleazziRFossenT (2015) Experimental verification of a global exponential stable nonlinear wave encounter frequency estimator. Ocean Engineering97: 48–56.
4.
BoydLBridsonR (2012) Multiflip for energetic two-phase fluid simulation. ACM Transactions on Graphics31(2): 1–12.
5.
ChenCSasaKPrpić-OršićJ (2021) Statistical analysis of waves’ effects on ship navigation using high-resolution numerical wave simulation and shipboard measurements. Ocean Engineering229: 108757.
6.
ChenDZhangJLiZ (2022) A novel fixed-time trajectory tracking strategy of unmanned surface vessel based on the fractional sliding mode control method. Electronics11(5): 726.
7.
Cruz-OrtizDChairezIPoznyakA (2022) Sliding-mode control of full-state constraint nonlinear systems: A barrier Lyapunov function approach. IEEE Transactions on Systems, Man, and Cybernetics: Systems52(10): 6593–6606.
8.
DingZWangHSunY (2022) Adaptive prescribed performance second-order sliding mode tracking control of autonomous underwater vehicle using neural network-based disturbance observer. Ocean Engineering260: 111939.
9.
FaheyKMillerM (2017) Unmanned systems integrated roadmap 2017-2042. Report, Department of Defense, Washington, DC, June.
10.
FengZPanZChenW (2022) USV application scenario expansion based on motion control, path following and velocity planning. Machines10(5): 310.
11.
FossenT (2002) Marine Control Systems: Guidance, Navigation, and Control of Ships, Rigs and Underwater Vehicles. Trondheim: Marine Cybernetics.
12.
FuMWangL (2021) Adaptive finite-time event-triggered control of marine surface vehicles with prescribed performance and output constraints. Ocean Engineering238: 109712.
13.
HuXDuJ (2018) Robust nonlinear control design for dynamic positioning of marine vessels with thruster system dynamics. Nonlinear Dynamics94(1): 365–376.
14.
HuangCZhangXZhangG (2019) Improved decentralized finite-time formation control of underactuated USVs via a novel disturbance observer. Ocean Engineering174: 117–124.
15.
HuangTXueYXueZ (2024) USV-tracker: A novel USV tracking system for surface investigation with limited resources. Ocean Engineering312: 119196.
16.
JahanshahiHYaoQKhanM (2023) Unified neural output-constrained control for space manipulator using tan-type barrier Lyapunov function. Advances in Space Research71(9): 3712–3722.
17.
JournéeJMassieW (2001) Offshore Hydromechanics. Delft: Delft University of Technology.
18.
LiJZhangGZhangX (2023) Integrating dynamic event-triggered and sensor-tolerant control: Application to USV-UAVs cooperative formation system for maritime parallel search. IEEE Transactions on Intelligent Transportation Systems24(7): 7258–7269.
19.
MoskowitzL (1964) Estimates of the power spectrums for fully developed seas for wind speeds of 20 to 40 knots. Journal of Geophysical Research69(24): 5161–5179.
20.
MuDFengYWangG (2022a) State-unknown single parameter learning adaptive output feedback control for ship dynamic positioning. Ocean Engineering266: 112811.
21.
MuDLangZFanY (2022b) Simulation and control applications based on pm wave spectra. In: 2022 7th international conference on automation, control and robotics engineering, Xi’an, China, 14–16 July, pp. 109–114. London: IEEE.
22.
NgoKMahonyRJiangZ (2005) Integrator backstepping using barrier functions for systems with multiple state constraints. In: Proceedings of the 44th IEEE conference on decision and control, Seville, 15 December, pp. 8306–8312. New York: IEEE.
23.
SongSParkJZhangB (2020) Event-triggered adaptive practical fixed-time trajectory tracking control for unmanned surface vehicle. IEEE Transactions on Circuits and Systems II: Express Briefs68(1): 436–440.
24.
SørensenASagatunSFossenT (1996) Design of a dynamic positioning system using model-based control. Control Engineering Practice4(3): 359–368.
25.
TongH (2023) An adaptive error constraint line-of-sight guidance and finite-time backstepping control for unmanned surface vehicles. Ocean Engineering285: 115298.
26.
WangNSunZYinJ (2019) Fuzzy unknown observer-based robust adaptive path following control of underactuated surface vehicles subject to multiple unknowns. Ocean Engineering176: 57–64.
27.
ZhangGZhangXZhengY (2015) Adaptive neural path-following control for underactuated ships in fields of marine practice. Ocean Engineering104: 558–567.
28.
ZhangJChenLWuX (2022) Robust adaptive control of marine vehicles with input saturation and output constraints. Ocean Engineering243: 110291.
29.
ZhuGMaYLiZ (2021) Adaptive neural output feedback control for MSVs with predefined performance. IEEE Transactions on Vehicular Technology70(4): 2994–3006.
