To address the challenges posed by unmodeled dynamics, lateral drift, external disturbances, and the difficulty in measuring velocities in underactuated surface vessels (USVs) motion systems, an active disturbance rejection control (ADRC) with two linear extended state observers (LESO) is proposed for USV path following. Firstly, we employ the Backstepping technique to create a virtual heading angle while estimating lateral drift and unmodeled dynamics using LESO. Subsequently, the ADRC algorithm is employed to control the heading angle. Since measuring the velocities of USV is challenging, velocity observers are introduce to estimate surge and sway velocities. Finally, we utilize the MMG model with high precision to track straight-line and curved paths, respectively. Simulation results validate that our developed controller accurately follows the reference path even in the presence of disturbances, affirming the effectiveness of our proposed control strategy.
LiJZhangGZhangW, et al. Cooperative path following control of USV-UAVs considering low design complexity and command transmission requirements. IEEE Trans Intell Veh2024; 9(1): 715–724.
2.
DoKDPanJ. Global robust adaptive path following of underactuated ships. Automatica2006; 42(10): 1713–1722.
3.
FossenTIPettersenKYGaleazziR. Line-of-sight path following for Dubins paths with adaptive sideslip compensation of drift forces. IEEE Trans Control Syst Technol2015; 23(2): 820–827.
4.
DengYZhangXZhangG. Line-of-sight-based guidance and adaptive neural path-following control for sailboats. IEEE J Oceanic Eng2020; 45(4): 1177–1189.
5.
HuXWeiXKaoY, et al. Robust synchronization for under-actuated vessels based on disturbance observer. IEEE Trans Intell Transp Syst2022; 23(6): 5470–5479.
6.
HuXWeiXZhangH, et al. Robust adaptive tracking control for a class of mechanical systems with unknown disturbances under actuator saturation. Int J Robust Nonlinear Control2019; 29(6): 1893–1908.
7.
LiZBuR. Trajectory tracking of under-actuated ships based on optimal sliding mode control with state observer. Ocean Eng2021; 233: 109186.
8.
LiuYBuRGaoX. Ship trajectory tracking control system design based on sliding mode control algorithm. Pol Marit Res2018; 25(3): 26–34.
9.
ZhangHZhangXBuR. Radial basis function neural network sliding mode control for ship path following based on position prediction. JMSE2021; 9(10): 1055.
10.
ZhipengSChangshengDNingZ. Trajectory tracking control of underactuated ship based on adaptive iterative sliding mode. J Traffic Transp Eng2017; 17(6): 125–134.
11.
LiRHuangJPanX, et al. Path following of underactuated surface ships based on model predictive control with neural network. Int J Adv Robot Syst2020; 17(4): 10.
12.
LiZLiRBuR. Path following of under-actuated ships based on model predictive control with state observer. J Mar Sci Technol2021; 26(2): 408–418.
13.
LiuCNegenbornRRChuX, et al. Predictive path following based on adaptive line-of-sight for underactuated autonomous surface vessels. J Mar Sci Technol2018; 23(3): 483–494.
14.
FossenTIBreivikMSkjetneR. Line-of-sight path following of underactuated marine craft. IFAC Proc Vol2003; 36(21): 211–216.
15.
LekkasAMFossenTI. Integral LOS path following for curved paths based on a monotone cubic Hermite spline parametrization. IEEE Trans Control Syst Technol2014; 22(6): 2287–2301.
16.
LekkasAMFossenTI. A time-varying lookahead distance guidance law for path following. IFAC Proc Vol2012; 45(27): 398–403.
17.
MoreiraLFossenTIGuedes SoaresC. Path following control system for a tanker ship model. Ocean Eng2007; 34(14–15): 2074–2085.
18.
WangNSunZYinJ, et al. Finite-time observer based guidance and control of underactuated surface vehicles with unknown sideslip angles and disturbances. IEEE Access2018; 6: 14059–14070.
19.
WangYTongHWangC. High-gain observer-based line-of-sight guidance for adaptive neural path following control of underactuated marine surface vessels. IEEE Access2019; 7: 26088–26101.
20.
ParkJSandbergIW. Universal approximation using radial-basis-function networks. Neural Comput1991; 3(2): 246–257.
21.
TingtingLJinkunL. Adaptive sliding mode control using RBF network. J Beijing Univ Aeronaut Astronaut2005; 31(10): 1106–1109.
22.
ChenBLiuKLiuX, et al. Approximation-based adaptive neural control design for a class of nonlinear systems. IEEE Trans Cybern2014; 44(5): 610–619.
HuangCZhangXZhangG. Adaptive neural finite-time formation control for multiple underactuated vessels with actuator faults. Ocean Eng2021; 222: 108556.
25.
WangHLiuKLiS. Command filter based globally stable adaptive neural control for cooperative path following of multiple underactuated autonomous underwater vehicles with partial knowledge of the reference speed. Neurocomputing2018; 275: 1478–1489.
26.
YinJ-CPerakisANWangN. A real-time ship roll motion prediction using wavelet transform and variable RBF network. Ocean Eng2018; 160: 10–19.
27.
LiJZhangGZhangX, et al. Event-triggered robust adaptive control for path following of the URS in presence of the marine practice. Ocean Eng2021; 242: 110139.
28.
ZhangGZhangCLiJ, et al. Improved composite learning path-following control for the underactuated cable-laying ship via the double layers logical guidance. Ocean Eng2020; 207: 107342.
29.
ZongxuanLRenxiangBHuganZ. Path following of ship based on sliding mode control with improved RBF neural network and virtual circle. J Northwest Polytech Univ2021; 39(1): 216–223.
30.
LiRLiTBuR, et al. Active disturbance rejection with sliding mode control based course and path following for underactuated ships. Math Probl Eng2013; 2013: 1–9.
31.
ZhangHZhangXBuR. Active disturbance rejection control of ship course keeping based on nonlinear feedback and ZOH component. Ocean Eng2021; 233: 109136.
32.
LiRJiaBFanS, et al. A novel active disturbance rejection control with hyperbolic tangent function for path following of underactuated marine surface ships. Meas Control2020; 53(9–10): 1579–1588.
33.
ZhangHZhangXXuH, et al. Heterogeneous cooperative trajectory tracking control between surface and underwater unmanned vehicles. Ocean Eng2024; 301: 117137.
34.
GuNWangDPengZ, et al. Advances in line-of-sight guidance for path following of autonomous marine vehicles: an overview. IEEE Trans Syst Man Cybern Syst2023; 53(1): 12–28.
35.
HanJ. From PID to active disturbance rejection control. IEEE Trans Ind Electron2009; 56(3): 900–906.
36.
GaoZ. Scaling and bandwidth-parameterization based controller tuning. In: Proceedings of the 2003 American control conference, Denver, CO, 4–6 June 2003, pp.4989–4996. New York: IEEE.
37.
ZhangHZhangXCaoT, et al. Active disturbance rejection control for ship path following with Euler method. Ocean Eng2022; 247: 110516.
38.
WangJZouZWangT. Path following of a ship sailing in restricted waters based on an extended updated-gain high-gain observer. In: Proceedings of the ASME 2018 37th international conference on ocean, offshore and arctic engineering. Volume 7B: Ocean Engineering, Madrid, Spain, 17–22 June 2018, p.V07BT06A030. New York: American Society of Mechanical Engineers.
39.
XiaobinQYongYHelongS, et al. Simulation system for testing ship dynamic positioning control algorithm. J Syst Simul2016; 28(9): 2028–2034.
