Particle Swarm Optimization of a Passivity-Based Controller for Dynamic Positioning of Ships

Ryalat, Mutaz; Damiri, Hazem Salim; ElMoaqet, Hisham

doi:10.3390/app10207314

Cited by 6 publications

(4 citation statements)

References 48 publications

(88 reference statements)

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“…Another category for meta-heuristic algorithms is inspired by physics, like the gravitational search algorithm [42]. Other meta-heuristic algorithms category is swarm-based, like Fish Search Optimization FSO and particle swarm optimization PSO [43,44].…”

Section: Literature Reviewmentioning

confidence: 99%

Gorilla tropical optimization algorithm solution for performance enhancement of offshore wind farm

Mohamed

Hasanien

Al‐Ammar

et al. 2023

IET Generation Trans & Dist

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This article describes a technique for improving the performance of an offshore wind farm (OWF), which includes a permanent magnet synchronous generator (PMSG) driven by a variable‐speed wind turbine (VSWT) and connected to the grid through a high‐voltage direct current transmission system (HVDC). Under various network disturbances, the gorilla tropical optimization (GTO) metaheuristic method is used to fine‐tune the proportional‐integral (PI) controller gains for VSC‐based PMSG‐VSWT and VSC‐based HVDC transmission systems. This study compares several strategies for extracting the maximum power from a system while maintaining stability under symmetrical and unsymmetrical fault circumstances. Compared to different algorithms, GTO provides better results and improves system recovery and stability after disturbances. As a consequence, dynamic and transient stability may be enhanced. MATLAB/Simulink is used to implement the control mechanism.

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Section: Literature Reviewmentioning

confidence: 99%

Gorilla tropical optimization algorithm solution for performance enhancement of offshore wind farm

Mohamed

Hasanien

Al‐Ammar

et al. 2023

IET Generation Trans & Dist

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“…The simulation results showed that the proposed method could eliminate body frame errors compared with traditional methods. In [12], Ryalat et al proposed an Interconnection and Damping Assignment-Passivity-Based Control (IAD-PBC) controller and used PSO to obtain the gains of the IDA-PBC controller which provides an effective way to tune these gains, thus improving the control performance of DP system. The simulation showed that the states with PSO converged to the desired position and heading angle and had less overshooting.…”

Section: Intelligent Algorithmmentioning

confidence: 99%

Ship Dynamic Positioning Control Based on Active Disturbance Rejection Control

Chen

Gao

et al. 2022

JMSE

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Nowadays, Dynamic Positioning (DP) is applied to various tasks such as subsea pipeline laying and the requirements for the positioning performance in marine operations are higher and higher. The main objective of this paper is to design a DP controller based on the Active Disturbance Rejection Control (ADRC) to solve the problems of long response time, large overshoot and low positioning accuracy in ship positioning. Firstly, the mathematical models of the ship and environmental disturbances are established. Secondly, the basic principle of ADRC is described. Meanwhile, stability analysis of the control system is introduced. Thirdly, ADRC is improved by the fal function filter and phase prediction method which solve the problem of dither and phase delay in tracking differentiators. Finally, simulations are carried out to verify the performance of the designed ADRC and the improved ADRC. Several simulation results show that the designed ADRC can realize the fixed-point control of the ship, which effectively solves the problems of long response time, overshoot and positioning accuracy, and compared with the traditional ADRC, the improved ADRC can reduce the error of straight track control, which indicates that ADRC can meet the requirements for the positioning performance and has a strong application value.

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“…e end-to-end network delay will increase, which will also lead to a decline in link utilization, a sharp drop in throughput, and even paralysis of the entire network system. In recent years, more and more scholars have begun to pay attention to and start to study the problem of network congestion, hoping to find ways and means to effectively solve such problems [14][15][16][17].…”

Section: Related Workmentioning

confidence: 99%

Computer Network Dynamic Balance Flow Distribution Based on Closed-Loop Particle Swarm Feedback Model

Zhou¹

2021

Mathematical Problems in Engineering

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Based on the closed-loop particle swarm feedback model, this paper proposes a graphical method to analyze the stability of the computer network dynamic balance system. First, based on the second-order time delay system model of congestion control, the stability of the system is described by characteristic pseudopolynomials. Secondly, based on the inverse line, the stability of the system is verified by graphical analysis methods, and the PID controller parameter range that guarantees the stability of the system is obtained, and the relationship between the controller proportional gain boundary and the network characteristic parameters is analyzed. Then, based on the analysis of the basic particle swarm optimization algorithm, the particle swarm evolution formula is divided into two parts, its own factors and social factors, and the influence of each part on the evolution speed and position of the particle swarm is analyzed, and an improved particle swarm is proposed. Finally, according to the above analysis, we find the corresponding equation from the appropriate solution in turn, thereby designing a class of particle swarm optimization algorithm with fewer intermediate variables. In view of the system involved in the classical PID control parameter tuning method, the improved particle swarm algorithm is applied to the parameter tuning and optimization of the PID controller. During the experiment, the improved PSO-PID controller optimization algorithm was used in the random early detection algorithm of active queue management, the process of the improved algorithm was researched and designed, and the relevant performance of the improved algorithm was verified through simulation experiments.

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Particle Swarm Optimization of a Passivity-Based Controller for Dynamic Positioning of Ships

Cited by 6 publications

References 48 publications

Gorilla tropical optimization algorithm solution for performance enhancement of offshore wind farm

Gorilla tropical optimization algorithm solution for performance enhancement of offshore wind farm

Ship Dynamic Positioning Control Based on Active Disturbance Rejection Control

Computer Network Dynamic Balance Flow Distribution Based on Closed-Loop Particle Swarm Feedback Model

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