In this paper, a novel decentralized adaptive coordinated control approach is proposed for human–robot mooring system (HRMS) under the communication topology of the network. First, to achieve output feedback control of multiple robots and ensure uniform, exponential, and global convergence of estimation errors, a distributed filter is proposed for HRMS to eliminate the need for velocity measurement information. Then, instead of modifying the control structure, a universal time-varying asymmetric barrier function (UTABF) is designed to directly confine the system output. It can handle both constrained and unconstrained instances equally. Moreover, adaptive neural networks (NNs) backstepping controllers are proposed to handle the nonlinearities and uncertainties of the system dynamics. The goal of achieving globally uniformly ultimately bounded (GUUB) is achieved by introducing a switch function. Finally, simulation experiments of HRMS composed of a ship model (60,000DWT bulk carrier) are provided to verify the effectiveness of the proposed control algorithms.
BechlioulisCPRovithakisGA (2009) Adaptive control with guaranteed transient and steady state tracking error bounds for strict feedback systems. Automatica45(2): 532–538.
2.
BrownPNMardenJR (2017) Optimal mechanisms for robust coordination in congestion games. IEEE Transactions on Automatic Control63(8): 2437–2448.
3.
ChenZGuoQLiT, et al. (2022) Distributed adaptive impedance control of networked Lagrangian systems with neighborhood interaction feedback. International Journal of Robust and Nonlinear Control32(4): 2251–2272.
4.
ChenZGuoQLiT, et al. (2023) Output constrained control of lower limb exoskeleton based on knee motion probabilistic model with finite-time extended state observer. IEEE/ASME Transactions on Mechatronics28: 2305–2316.
5.
DingSZhaoTGaoF, et al. (2022) Research on a motion-inhibition fuzzy control method for moored ship with multi-robot system. Ocean Engineering248: 110795.
6.
DingSZhaoTZhangW, et al. (2023) Motion-inhibition control of a multi-robot mooring system using an actuating force fuzzy control method. Ocean Engineering267: 113236.
7.
DongGYanMZhengZ, et al. (2022) Experimental investigation on the hydrodynamic response of a moored ship to low-frequency harbor oscillations. Ocean Engineering262: 112261.
8.
FarhadiFGolestaniSJTeneketzisD (2018) A surrogate optimization-based mechanism for resource allocation and routing in networks with strategic agents. IEEE Transactions on Automatic Control64(2): 464–479.
9.
GiustiAMalzahnJTsagarakisNG, et al. (2018) On the combined inverse-dynamics/passivity-based control of elastic-joint robots. IEEE Transactions on Robotics34(6): 1461–1471.
10.
HeWDavidAOYinZ, et al. (2016) Neural network control of a robotic manipulator with input deadzone and output constraint. IEEE Transactions on Systems, Man, and Cybernetics: Systems46(6): 759–770.
11.
HongYChenGBushnellL (2008) Distributed observers design for leader-following control of multi-agent networks. Automatica44(3): 846–850.
12.
HuangYMengZ (2021) Global finite-time distributed attitude synchronization and tracking control of multiple rigid bodies without velocity measurements. Automatica132: 109796.
13.
JiNLiuJ (2019) Adaptive actuator fault-tolerant control for a three-dimensional Euler–Bernoulli beam with output constraints and uncertain end load. Journal of the Franklin Institute356(7): 3869–3898.
14.
JimenezJFGiron-SierraJM (2020) USV based automatic deployment of booms along quayside mooring ships: Scaled experiments and simulations. Ocean Engineering207: 107438.
15.
JinX (2015) Adaptive fault-tolerant control for a class of output-constrained nonlinear systems. International Journal of Robust and Nonlinear Control25(18): 3732–3745.
16.
KimMJChoiYChungWK (2015) Bringing nonlinear ℋ∞ optimality to robot controllers. IEEE Transactions on Robotics31(3): 682–698.
17.
KouLChenZXiangJ (2022) Cooperative fencing control of multiple vehicles for a moving target with an unknown velocity. IEEE Transactions on Automatic Control67(2): 1008–1015.
18.
LiangXHuangXWangM, et al. (2010) Adaptive task-space tracking control of robots without task-space- and joint-space-velocity measurements. IEEE Transactions on Robotics26(4): 733–742.
19.
LiuCHChenYM (1986) Multi-microprocessor-based cartesian-space control techniques for a mechanical manipulator. IEEE Journal on Robotics and Automation2(2): 110–115.
20.
LiuHNiuBQinJ (2021) Reachability analysis for linear discrete-time systems under stealthy cyber attacks. IEEE Transactions on Automatic Control66(9): 4444–4451.
21.
MabroukM (2010) Triangular form for Euler–Lagrange systems with application to the global output tracking control. Nonlinear Dynamics60(1): 87–98.
22.
MedinaJRHircheS (2018) Considering uncertainty in optimal robot control through high-order cost statistics. IEEE Transactions on Robotics34(4): 1068–1081.
23.
PanteleyELefeberELoriaA, et al. (1998) Exponential tracking control of a mobile car using a cascaded approach. IFAC Proceedings Volumes31(27): 201–206.
24.
RenW (2009) Distributed leaderless consensus algorithms for networked Euler–Lagrange systems. International Journal of Control82(11): 2137–2149.
25.
WangMYangA (2017) Dynamic learning from adaptive neural control of robot manipulators with prescribed performance. IEEE Transactions on Systems, Man, and Cybernetics: Systems47(8): 2244–2255.
26.
WangYWangHFuM (2022) Prescribed performance trajectory tracking control of dynamic positioning ship under input saturation. Transactions of the Institute of Measurement and Control44(1): 30–39.
27.
YangYZhouXLiJ, et al. (2024) Barrier Function-Based Adaptive Composite Sliding Mode Control for a Class of MIMO Underactuated Systems Subject to Disturbances. IEEE Transactions on Industrial Informatics20(9): 11429–11437.
28.
YangYJiangHGanL, et al. (2024) Fixed-time composite neural learning control of flexible telerobotic systems. IEEE Transactions on Cybernetics54(6): 3602–3614.
29.
YuXDingNZhangA, et al. (2018) Cooperative moving-target enclosing of networked vehicles with constant linear velocities. IEEE Transactions on Cybernetics50(2): 798–809.
30.
ZengXYangYZhaoJ, et al.(2024). Fully distributed event-triggered control for multi-robot systems based on modal space framework. Nonlinear Dynamics, 112, 3605–3618.
31.
ZhangJYuXGuoL (2023a) Polarization navigation system/geomagnetic navigation system–assisted initial alignment method for ships under wind and wave conditions. Transactions of the Institute of Measurement and Control. Epub ahead of print July8. DOI: 10.1177/01423312231183523.
32.
ZhangSWuYHeX, et al. (2022) Cooperative fault-tolerant control for a mobile dual flexible manipulator with output constraints. IEEE Transactions on Automation Science and Engineering19(4): 2689–2698.
33.
ZhangYKeliPChangchunH (2023b) Global composite learning fixed-time control of robotic systems with asymmetric tracking error constraints. IEEE Transactions on Industrial Electronics70(7): 7082–7091.
34.
ZhaoKSongY (2019) Removing the feasibility conditions imposed on tracking control designs for state-constrained strict-feedback systems. IEEE Transactions on Automatic Control64(3): 1265–1272.
35.
ZhaoTDingSGaoF, et al. (2022) Research on a novel robot mooring system based on dual-parallel elastic under-actuated mechanisms. Applied Ocean Research119: 103020.
36.
ZhaoYDuanZWenG (2015) Distributed finite-time tracking of multiple Euler–Lagrange systems without velocity measurements. International Journal of Robust and Nonlinear Control25(11): 1688–1703.
37.
ZhengJYanMLiY, et al. (2021) An online identification approach for a nonlinear ship motion model based on a receding horizon. Transactions of the Institute of Measurement and Control43(13): 3000–3012.
38.
ZhuCJiangYYangC (2023) Fixed-time neural control of robot manipulator with global stability and guaranteed transient performance. IEEE Transactions on Industrial Electronics70(1): 803–812.
