Proceedings of the Thirty-second (2022) International Ocean and Polar Engineering ConferenceShanghai, China, June 5–10, 2022
Copyright © 2022 by the International Society of Offshore and Polar Engineers (ISOPE)
ISBN 978-1-880653-81-4; ISSN 1098-6189

www.isope.org

Robust Adaptive Neural Tracking Control for Dynamic Positioning of Vessels
with Input Saturation and Output Constraints
Quan Shi1,2, Changjun Hu1,2, Xiaoxian Guo1,2, Jianmin Yang1,2, Xin Li1,2*1

State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai, China
2 SJTU Yazhou Bay Institute of Deepsea Technology, Sanya, Hainan, China*Corresponding author

execute sudden large commands with large errors, while the guidancesystem can compute the appropriate smooth reference signal for thevessel to follow subject to mission or geometric constraints, e.g., thedesired path or trajectory, intended to circumvent undesired motion
behavior of the vessel (Fossen, 2011). Therefore, the guidance-based
DP tracking control for DP of vessels is more practical than the DP
control directly.

ABSTRACTThe global exploration and exploitation of the abundant resources inthe deep sea has sparked interest in the study of dynamic positioning(DP) systems. This study focuses on the robust adaptive neural network(NN) tracking control design for DP of vessels in the presence of model
uncertainties, environmental disturbances, input saturation, and output
constraints. The guided DP tracking control problem is first addressed
based on reference filters. Then, the NN technology and robust term arefused into vectorial backstepping procedure to realize the adaptability
to uncertainties and the robustness against disturbances. To satisfy
prescribed output constraint specifications on tracking errors,prescribed performance control (PPC) method is integrated into
proposed control design. In addition, an auxiliary system is used tocope with actuator saturation, which can reduce the abrasion of theactuators. Finally, to verify the effectiveness of the proposed control
design, all comparative studies are performed in a joint high-fidelity
simulation framework, and the simulation results on a shuttle tanker
illustrate the effectiveness and superiority of the proposed DP control
strategy.

In the DP control community, the research on DP ship control under
the influence of platform uncertainties and environmental loads has
been an enduring topic in recent decades. Many advanced methods areused in DP control to solve the above problems, such as robust control(Du et al., 2016), adaptive control (Li et al., 2020), NN control (Souissiet al., 2020), fuzzy logic control (Hao et al., 2021), and disturbanceobserver (Cui et al., 2021), etc. However, it is worth noting that many
restrictions or assumptions are introduced into the controller design,e.g., the requirements for accurate model parameters or priorknowledge of disturbances. To circumvent these problems, the abovementioned control methods are fused together (Hu et al., 2018).
The emerging advanced control scheme provides the possibility tosolve more practical problems. In the theoretical study of DP control,
the sway force, sway force, and yaw moment are generally conceived
as control inputs, whereas ignoring the dynamics and input saturation
effects of actual thrusters (channel propeller, azimuth propeller, rudder,etc.) (Cui et al., 2021; Gao et al., 2021; Souissi et al., 2020). The
auxiliary system is verified to be effective in dealing with the input
saturation problem of the system (Du et al., 2016; Hu et al., 2018). On
the other hand, mainstream methods to prevent constraint violation,
including artificial potential field (APF) (Peng et al., 2019; Shi et al.,2019), model predictive control (MPC) (Liu et al., 2021), and error
transformation methods represented by PPC (He et al., 2021a; He et al.,2021b) and Barrier Lyapunov Function (BLF) (Tee et al., 2009). PPC
methods have been widely adopted in DP control design with thecharacteristics of low computational burden and preset exponentialconvergence. Especially, the application of PPC to the backstepping
control framework yields a new means of addressing the output
constraints of DP systems, e.g., exponentially decreasing performance

KEY WORDS: Dynamic Positioning, Tracking Control, Output
Constraints, Neural Network, High-fidelity Simulation.

INTRODUCTION
The DP control system can continuously activate the its propellers toresist the sea loads (wind, waves, currents, etc.), and automaticallycontrol the position/heading of vessel (Peng et al., 2021). With therapid development of industry and the ever-increasing demand formarine resources, more and more vessels serving complex maritime
segments are also equipped or retrofitted with DP capabilities, such as
semi-submersibles for floating installations.
Broadly speaking, not only limited to station-keeping, DP control of
low-speed maneuvering operations also cater to practical requirements(Sørensen, 2011). For large changes in the setpoint, the controller will2160