Real-Time Weight Optimization of a Nonlinear Model Predictive Controller Using a Genetic Algorithm for Ship Trajectory Tracking

Yu, Dunjing; Deng, Fang; Wang, Hongyan; Hou, Xiuhui; Yang, Hualin; Shan, Tikun

doi:10.3390/jmse10081110

Cited by 6 publications

(3 citation statements)

References 28 publications

(36 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Dunjing Yu et al used GA to optimize the nonlinear predictive controller of the ship trajectory tracking model. The experiments showed that GA improved the efficiency and accuracy of the controller [54].…”

Section: Literature Reviewmentioning

confidence: 98%

Prediction of Waterway Cargo Transportation Volume to Support Maritime Transportation Systems Based on GA-BP Neural Network Optimization

2022

View full text Add to dashboard Cite

Water transportation is an important part of comprehensive transportation and plays a critical role in a country’s economic development. The world’s cargo transportation is dominated by waterway transportation, and maritime transportation Systems (MTS) are the main part of the waterway transportation system. The flow of goods plays a key role in the economic development of the ports along the route. The sustainable development of maritime transportation, the maritime transportation economy and the environment have great practical significance. In this paper, the principle of the BP (back propagation) neural network is used to predict the freight transportation volume of China’s waterways, and the genetic algorithm (GA) is used to optimize the BP neural network, so as to construct the GA-BPNN (back propagation neural network) prediction model. By collecting and processing the data of China’s water cargo transport volume, the experimental results show that prediction accuracy is significantly improved, which proves the reliability of the method. The experimental methods and results can provide certain reference information for the optimization, upgrade, and more scientific management of sustainable MTS in China and internationally, provide key information for port cargo handling plans, help optimize port layout, and improve transportation capacity and efficiency.

show abstract

Section: Literature Reviewmentioning

confidence: 98%

Prediction of Waterway Cargo Transportation Volume to Support Maritime Transportation Systems Based on GA-BP Neural Network Optimization

2022

View full text Add to dashboard Cite

show abstract

“…Many scholars have also studied the above two problems. Yu et al 22 proposed an improved MPC controller that can dynamically adjust the weight matrix in the objective function according to the reference trajectory curvature so as to improve the tracking accuracy of the trajectory. However, it is difficult to balance multiple constraints of the system by just changing the size of the weight matrix overall rather than adjusting individual weights in the weight matrix according to the trajectory changes.…”

Section: Introductionmentioning

confidence: 99%

Dynamic positioning for semi-submersible platform using stable fuzzy model predictive control

Wan

2023

Proceedings of the Institution of Mechanical Engineers, Part I:

View full text Add to dashboard Cite

More and more semi-submersible platforms are being employed in the deep-sea industry as a result of the offshore oil and gas industry’s rapid growth, which also raises the bar for the positioning capabilities of semi-submersible platforms’ positioning systems. Model predictive control is frequently used in dynamic positioning control because it is effective at addressing a variety of limitations. Yet, there are still issues to be resolved regarding how to enhance model predictive control’s robustness under various working settings and how to offer workable solutions for model predictive control under stable conditions. The approach of stable fuzzy model predictive control for the dynamic placement of a semi-submersible platform is suggested in this research based on this. This method increases the tracking accuracy and tracking speed of dynamic positioning by incorporating the T-S fuzzy algorithm into model predictive control and allowing the coefficients in the weight matrix of the objective function to be modified adaptively. In addition, the solution is limited to the feasible domain by the addition of the control Lyapunov function, ensuring the stability and efficiency of the entire control procedure. A semi-submersible platform’s fixed-point control and trajectory tracking can be accomplished using the proposed technique, which has better trajectory tracking effects than the conventional model predictive control, according to comprehensive simulation findings.

show abstract

“…[18] employed a nonlinear model predictive control (NMPC) model based on an accurate ship motion model to control the trajectory tracking, efficiently improving the anti-disturbance capability. [21] presented a weight optimization method for a nonlinear MPC (NMPC) based on the genetic algorithm (GA) for ship trajectory tracking. [7] used the model predictive control (MPC) method under constraints to track planned trajectories and optimize motion processes, while ensuring control stability through Lyapunov's theorem.…”

Section: Introductionmentioning

confidence: 99%

Ship Path-Tracking with Real Disturbance and Exponential Cost Function

Zhang,

Li,

Luo

et al. 2023

Preprint

View full text Add to dashboard Cite

Intelligent ship technology is currently an international research hotspot, and model predictive control is widely used in the path-tracking control of intelligent vehicles. To construct an intelligent ship anti-disturbance path-tracking control method, firstly, an environmental disturbance model was constructed with the actual meteorological data of the target sea area. Secondly, the Fossen ship equation of motion is linearized and discretized as the ship motion model. Thirdly, the expression of the prediction equation is derived from the ship motion model. Fourthly, the cost function is constructed by using the polar diameter and polar angle values of the ship. Fifth, the power function in the cost function is replaced with an exponential function to obtain an improved cost function. Sixthly, according to the Lyapunov theory and the MPC terminal constraint theory, the stability of the improved cost function is verified. Seventh, different test paths are set up, the environmental disturbance model is taken as the external disturbance, the ship motion model, the prediction equation, and the improved cost function are used to design the anti-disturbance path-tracking control algorithm according to the model prediction control idea for simulation experiments. Finally, different MATLAB simulation results show that the improved cost function can resist disturbance of the external wave, current, and wind, and effectively track the target path. Therefore, this study provides a reference for improving the navigation safety of ship path-tracking.

show abstract

Real-Time Weight Optimization of a Nonlinear Model Predictive Controller Using a Genetic Algorithm for Ship Trajectory Tracking

Cited by 6 publications

References 28 publications

Prediction of Waterway Cargo Transportation Volume to Support Maritime Transportation Systems Based on GA-BP Neural Network Optimization

Prediction of Waterway Cargo Transportation Volume to Support Maritime Transportation Systems Based on GA-BP Neural Network Optimization

Dynamic positioning for semi-submersible platform using stable fuzzy model predictive control

Ship Path-Tracking with Real Disturbance and Exponential Cost Function

Contact Info

Product

Resources

About