Robust nonlinear model predictive control for reference tracking of dynamic positioning ships based on nonlinear disturbance observer

Predictive compensator based event-triggered model predictive control with nonlinear disturbance observer for unmanned surface vehicle under cyber-attacks

In order to further explore more efficient identification algorithms that can solve the ship motion identification modeling problem, a novel identification algorithm for 3-DOF ship maneuvering modeling is proposed in this paper. Based on the recursive least-squares method, the proposed algorithm combines multi-innovation and nonlinear innovation techniques that focus on the innovation’s processing. In addition, its convergence capability is theoretically demonstrated. The identification algorithm shows good accuracy and convergence that is verified by a simulation experiment on a container ship. Furthermore, another simulation experiment using the full mission ship handling simulator demonstrates its generalization capability. 

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A Novel Parameter Identification Algorithm for 3-DOF Ship Maneuvering Modelling Using Nonlinear Multi-Innovation

An enhanced trajectory tracking control of the dynamic positioning ship based on nonlinear model predictive control and disturbance observer

This paper surveys the recent advances in dynamic positioning (DP) control for marine crafts. DP of marine crafts means that a craft can maintain a fixed position and heading, or move along a predetermined trajectory slowly without the anchoring system, using only its own thruster system to counteract ocean disturbances. The survey is by no means exhaustive but provides a survey of some of the major technological advancements in DP controller design over the years of research and development. Firstly, the model of marine crafts and some difficult problems in DP control are introduced including the impact of multiple source disturbance, unavailable velocity measurement information, resource conservation and performance optimization, destabilizing impact of faults and network security and compound multi-constraint restrictions. Then, the DP control schemes in recent years are summarized and classified in detail. Finally, some theoretical and technical problems are proposed, including online data-driven model-free control, man–machine combination intelligent control and composite hierarchical anti-disturbance control to guide future investigations. 

Dynamic Positioning Control for Marine Crafts: A Survey and Recent Advances

This paper presents the schemes of nonlinear offset-free model predictive control (MPC) for reference tracking of ship dynamic positioning (DP) systems, in the presence of slow-varying stochastic disturbances and input constraints. Two offset-free MPC strategies for nonlinear DP systems are proposed. The first approach, namely, the target calculation formulation, estimates the disturbance based on the augmented disturbance model, and employs a target calculator to address the MPC optimization problem. The second approach, namely, the delta input formulation, works with the augmented velocity model to lump the effects of disturbances into the input estimates. By successively on-line linearizing the state-space model at the current operating point, the future outputs are explicitly predicted, and then the nonlinear optimization problem becomes an easy quadratic optimization problem. The unscented Kalman filter is adopted for the state estimation. By implementing simulations for two scenarios of disturbances with parametric plant-model mismatch, the effectiveness of the two strategies is demonstrated. Results show that the closed-loop control performance of the delta input formulation method is superior, for its good robustness to the stochastic disturbance.

UKF Based Nonlinear Offset-free Model Predictive Control for Ship Dynamic Positioning under Stochastic Disturbances

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UKF Based Nonlinear Offset-free Model Predictive Control for Ship Dynamic Positioning under Stochastic Disturbances