Result: Modeling and Trajectory Tracking Model Predictive Control Novel Method of AUV Based on CFD Data.

Title:
Modeling and Trajectory Tracking Model Predictive Control Novel Method of AUV Based on CFD Data.
Authors:
Bao H; College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, China., Zhu H; Yantai Research Institute and Graduate School, Harbin Engineering University, Yantai 265501, China.
Source:
Sensors (Basel, Switzerland) [Sensors (Basel)] 2022 Jun 01; Vol. 22 (11). Date of Electronic Publication: 2022 Jun 01.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220 (Electronic) Linking ISSN: 14248220 NLM ISO Abbreviation: Sensors (Basel) Subsets: PubMed not MEDLINE; MEDLINE
Imprint Name(s):
Original Publication: Basel, Switzerland : MDPI, c2000-
References:
Sensors (Basel). 2020 Sep 01;20(17):. (PMID: 32882926)
IEEE Trans Neural Netw Learn Syst. 2021 May 31;PP:. (PMID: 34057897)
Grant Information:
202006680065 China Scholarship Council; "Underwater Vehicles Key Technology R\&D Center" The university-local integration category project
Contributed Indexing:
Keywords: GA-ACO algorithm; autonomous underwater vehicle; hydrodynamic analysis; model predictive control; normal probability division; trajectory tracking
Entry Date(s):
Date Created: 20220610 Latest Revision: 20220716
Update Code:
20250114
PubMed Central ID:
PMC9185449
DOI:
10.3390/s22114234
PMID:
35684855
Database:
MEDLINE

Further Information

In this paper, a novel model predictive control (MPC) method based on the population normal probability division genetic algorithm and ant colony optimization (GA-ACO) method is proposed to optimally solve the problem of standard MPC with constraints that generally cannot yield global optimal solutions when using quadratic programming (QP). Combined with dynamic sliding mode control (SMC), this model is applied to the dynamic trajectory tracking control of autonomous underwater vehicles (AUVs). First, the computational fluid dynamics (CFD) simulation platform ANSYS Fluent is used to solve for the main hydrodynamic coefficients required to establish the AUV dynamic model. Then, the novel model predictive controller is used to obtain the desired velocity command of the AUV. To reduce the influence of external interference and realize accurate velocity tracking, dynamic SMC is used to obtain the control input command. In addition, stability analysis based on the Lyapunov method proves the asymptotic stability of the controller. Finally, the trajectory tracking performance of the AUV in an underwater, three-dimensional environment is verified by using the MATLAB/Simulink simulation platform. The results verify the effectiveness and robustness of the proposed control method.