A Thrust Allocation Algorithm considering Dynamic Positioning and Roll Damping Thrust Demands using multi-step Quadratic Programming

\n The Dynamic Positioning (DP) System is responsible for the station keeping of vessels in several offshore operations. The forces required by the DP System are distributed among the available thrusters by a thrust allocation algorithm which should be accurate, efficient and robust. This means that the effective forces match the required forces while power consumption is minimized. Additionally, in case of impossibility of generating the required forces the heading of the vessel is maintained to avoid increasing environmental forces. To accurately generate the required forces, the physical limitations of the thrusters and the hydrodynamic interactions must be considered. The hydrodynamic interactions are consistently modelled to accommodate the following typical effects: thruster-hull, thruster-current and thruster-thruster interaction. The result of this modeling is a nonlinear optimization problem, which is solved using the Sequential Quadratic Programming (SQP) algorithm with slack variables. The DP simulation carried out show that by considering the hydrodynamic interactions on thrust allocation the overall performance (controllability and power consumption) of the DP system is improved.\n

Dynamic Positioning simulations of a Thrust Allocation Algorithm considering Hydrodynamic Interactions

A dynamic positioning (DP) system is a computer-controlled system which maintains the positioning and heading of ship by means of active thrust. A DP system consist of sensors, observer, controller and thrust allocation algorithm. The purpose of this paper is to investigate the performance of proportional derivative type fuzzy controller with Mamdani interface scheme for dynamic positioning of an oceanographic research vessel (ORV) by numerical simulation. Nonlinear passive observer is used to filter the noise from the position and orientation. A nonlinear mathematical model of the ORV is subjected to the wave disturbance ranging from calm to phenomenal sea. Robustness and efficiency of the fuzzy logic controller is analysed in comparison with the multivariable proportional integral derivative (PID) and the linear quadratic regulator (LQR) controller. A simplified constrained linear quadratic algorithm is used for thrust allocation. The frequency response of the closed loop system with different controllers is analysed using the bode plot. The stability of controller is established using the Lyapunov criteria.

Using dynamic positioning (DP) ship maintains its position and heading by active thrusters. Several challenges exist with regard to the DP controller design, such as uncertain system model, actuator with saturation hard limitations, state or output (ship position or orientation) constraints. A robust receding horizon dual-mode control method is introduced into the design of DP controller using the property of the invariant ellipsoid. Outside the ellipsoid, the receding horizon control is employed .Once the state enters the ellipsoid, the control is switched to a linear state feedback control, which makes the state stay in the ellipsoid forever and stabilizes the system while satisfying the input, the state and the output constraints. The DP control is obtained by using semi-definite programming (SDP) which can be easily solved by means of linear matrix inequalities (LMI). At last, the simulation study is carried out to verify the feasibility and efficiency of the application of robust receding horizon dual-mode control for dynamic positioning ship.

In order to further improve the control ability of dynamic positioning ship or platform under complex sea conditions, the controller and thrust allocation system of dynamic positioning are designed. A simplified three-degree-of-freedom MMG mathematical model is established for the pod full-rotation propeller used in most dynamic positioning ships. On the basis of retaining the advantages of active disturbance rejection control, the sliding mode variable structure control method is adopted to design the control law. The objective function and constraints of thrust allocation are designed based on the lowest energy consumption, and the sequential quadratic programming algorithm is used to calculate the real-time thrust allocation of each propeller. The simulation results of the ship dynamic positioning system in MATLAB show that the controller can maintain the stability and robustness of the system under the condition of external environment disturbance, and the results of the thrust allocation are reasonable, which has a certain reference value for the further study of the ship dynamic positioning control system.KeywordsSliding mode controlDynamic positioning systemThrust allocationSequential quadratic programming

This study is concerned with the robust H ∞ control problem for dynamic positioning (DP) ships based on sampled-data. Input delay approach is used to convert the sampled-data DP ship system to a system with time-varying delay. Adequate condition is derived to determine the system's asymptotical stability and establish H ∞ performance. Then sampled-data controller is obtained by means of linear matrix inequalities. Simulation results show that the proposed method and the designed controller for DP ships are effective in the existence of varying environment disturbance.

Oil drilling and extraction platforms are currently being used in many offshore areas around the world. Whilst those operating in shallow seas are secured to the seabed, for deeper water operations, Dynamic Positioning (DP) is essential for the platforms to maintain their position within a safe zone. Operating DP requires intelligent and reliable control systems. Nearly all DP accidents have been caused by a combination of technical and human failures; however, according to the International Marine Contractors Association (IMCA) DP Incidents Analysis, DP control and thruster system failures have been the leading causes of incidents over the last ten years. This paper will investigate potential operational improvements for DP system accuracy by adding a Predictive Neural Network (PNN) control algorithm in the thruster allocation along with a nonlinear Proportional Integral derivative (PID) motion control system. A DP system’s performance on a drilling platform in oil and gas deep-water fields and subject to real weather conditions is simulated with these advanced control methods. The techniques are developed for enhancing the safety and reliability of DP operations to improve the positioning accuracy, which may allow faster response to a critical situation during DP drilling operations. The semisubmersible drilling platform’s simulation results using the PNN strategy show improved control of the platform’s positioning.

Following an investigation undertaken by the publisher, we have determined that this paper was accepted on the basis of a compromised peer review process. We hereby retract the paper. The corresponding author has been notified of the retraction. The retraction statement can be found here: https://doi.org/10.1520/JTE20269996. Aiming at the difficulty of the dynamic positioning process and trajectory control under the influence of complex disturbance, ship dynamic positioning control based on the nonlinear fuzzy algorithm is studied. By analyzing the composition and task of the ship dynamic positioning system, the nonlinear characteristics of the ship dynamic positioning system are obtained, and the ship dynamic pose is described by the combined coordinate system of the geodetic coordinate system, ship hull coordinate system, and multi-degree-of-freedom coordinate system. The dual fuzzy proportional, integral, derivative (PID) algorithm is applied to the point control and surface control and an approximate algorithm is adopted to calculate the target control parameters, so as to enhance the comprehensive effectiveness of ship hull nonlinear control results. The experimental results show that the control effect of this method is better and the heading angle control error is smaller. Meet the dynamic positioning ship control needs.

Thrust allocation (TA) is a vital part of the dynamic positioning (DP) system of vessels equipped with azimuth thrusters. It determines the control input of each thruster device according to the control command generalized by the DP controller, which has a great impact on the control accuracy, stability, and flexibility of marine operation. In order to solve the TA problem, a multi-agent distributed optimization method based on distributed TA quadratic programming (QP) model in Cartesian coordinates is proposed in this paper. The proposed method considers the power consumption, the mechanical wear, the mechanical constraints, and the singularity from the perspective of multi-agent. The simulation examples show that the method proposed in this paper can realize optimal TA effectively without error.

Small Autonomous Surface/Underwater Vehicles (S-ASUVs) are gradually attracting attention from related fields due to their small size, low energy consumption, and flexible motion. Existing dynamic positioning (DP) control approaches suffer from chronic restrictions that hinder adaptability to varying practical conditions, rendering performance poor. A new three-dimensional (3D) dynamic positioning control method for S-ASUVs is proposed to tackle this issue. Firstly, a dynamic model for the DP control problem considering thrust allocation was established deriving from dynamic models of S-ASUVs. A novel Lyapunov-based model predictive control (LBMPC) method was then designed. Unlike the conventional Lyapunov-based model predictive control (LMPC), this study used multi-variable proportional–integral–derivative (PID) control as the secondary control law, improving the accuracy and rapidity of the control performance significantly. Both the feasibility and stability were rigorously proved. A series of digital experiments using the S-ASUV model under diverse conditions demonstrate the proposed method’s advantages over existing controllers, affirming satisfactory performances for 3D dynamic positioning in complex environments.

Performances of a set of control schemes for dynamic positioning (DP) are studied in this work; DP performance is essential for future developments of autonomous shipping technology. The linear and non-linear model predictive control (MPC and NMPC), the non-linear proportional integral and derivative (NPID) control, the sliding mode control (SMC), as well as the multi-resolution PID (MRPID) control schemes, are evaluated under two different sea conditions, namely, moderate and extreme seas. Matlab/Simulink models of a full-scale ship and its corresponding scaled model are used to benchmark the efficacy of the controllers. An unscented Kalman filter (UKF) is used to estimate vessel motions and to control low frequency (LF) motions while filtering out wave frequency (WF) motions. The tuning of the controllers is also taken into consideration. Of the five controller schemes, the NMPC shows the best ability to deal with extreme disturbances efficiently. Although all of the controllers were able to maintain the ship position under moderate conditions, only the NMPC and the MRPID controllers were able to stabilize the ship under extreme sea states. Findings from this research are expected to help operators of DP systems in choosing the most effective control scheme for different sea conditions. In addition, the results are supportive of further control system development for dynamic positioning and autonomous operations of ships and offshore platforms.

A thrust allocation method with capabilities to assist the power management system on dynamically positioned ships is proposed in this paper. Its main benefits are reduction in frequency and/or load variations on the electric network, and a formulation of thruster bias which can be released when required by the power management system. To reduce load variations without increasing overall power consumption it is necessary to deviate from the thrust command given by the dynamic positioning system or joystick. The resulting deviation in position and velocity of the vessel is tightly controlled, and results show that small deviations are sufficient to fulfill the objective. For simplicity, the study has been limited to thrusters with fixed direction, having in mind that generalizations are fairly straightforward.

The drilling of the Mohole, a hole through the crust of the earth, will be undertaken from a drilling ship operating in true oceanic depths. Because anchoring at these depths is infeasible, the position of the drilling ship above the hole in the ocean floor must be controlled by surface propulsion units. This concept is called "dynamic positioning." An experimental drilling program was undertaken in 1961 to determine, among other engineering factors, whether dynamic positioning was feasible. A special integrated control system was developed for four steering propellers which permitted a single operator to control the position of the experimental drilling ship, CUSS I. The system operated successfully for a month at sea and proved that dynamic positioning was not only feasible but entirely practical. This system is expected to form the basis for the control of future waterborne vehicles for geophysical and oceanographic research.

This paper presents a model predictive controller for autonomous vehicles based on ultra-local models and a Nonlinear Extended State Observer (NESO) that simultaneously address path tracking performance and roll stability. The controller consists of several key components: first, ultra-local models are developed to represent the vehicle lateral error dynamics and roll dynamics. Next, a nonlinear extended state observer is designed to estimate integrated disturbances and unmodeled states within the ultra-local models. The parameters of the observer are optimized using the Butterfly Optimization Algorithm (BOA) in a simulation environment to enhance estimation accuracy. Finally, a model predictive control (MPC) scheme is implemented to minimize both lateral tracking error and roll angle, effectively addressing both path tracking performance and roll stability. The effectiveness of the proposed controller is validated through comprehensive comparative simulations conducted on a MATLAB/Simulink and Carsim co-simulation platform.

This article presents a method named pseudo-inverse to solve the optimal thrust allocation of dynamic positioning (DP) system, proposes to optimally determine the azimuth angle of thrusters instead of man-control or semi-auto control, and combines with the pseudo-inverse methods to get the optimal solutions for dynamic positioning control system. It is able to greatly reduce the risk of manual mode. Three different kinds of modes are proposed and detailedly illuminated, and can be used to solve much more complex nonlinear constraint problems, such as typical forbidden vector boundary. Several illustrative examples are provided to demonstrate the effectiveness and correctness of the proposed thrust allocation modes.