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

被引:1
|
作者
Wan, Min [1 ,2 ,4 ]
Du, Jiadai [1 ,2 ]
Yi, Hao [3 ]
机构
[1] Southwest Petr Univ, Sch Mech & Elect Engn, Chengdu, Peoples R China
[2] Southwest Petr Univ, Energy Equipment Inst, Chengdu, Peoples R China
[3] Univ Chinese Acad Sci, Beijing, Peoples R China
[4] Southwest Petr Univ, Sch Mech & Elect Engn, Chengdu 610500, Peoples R China
关键词
Semi-submersible platform; dynamic positioning; model predictive control; T-S fuzzy algorithm; control Lyapunov function; TO-STATE STABILITY; SYSTEM;
D O I
10.1177/09596518231182280
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:73 / 86
页数:14
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