Prediction of USV pose based on VMD-WHHO-BLS
GE Quan-bo
XUE Zi-jian
ZHANG Ming-chuan
WU Qing-tao
Abstract：With the development of artificial intelligence technology and the popularity of intelligent sensors in the field of unmanned control system,the operational data of various types of unmanned equipment is enriched.Unmanned surface vessel(USV)as an important component of unmanned intelligent equipment,the key link of unmanned aerial vehicle is to control its safety and stability independently.Because of its complex structure and long time operation in unknown environment,it is unavoidable that various abnormal conditions will occur,which will directly affect the capability of unmanned aerial vehicle and reduce its safety and economy.Therefore,it is necessary to accurately predict the unmanned ship's posture.Firstly,time series data is decomposed into several components by using variational mode decomposition,then several types of data in unmanned ship are predicted by adopting a broad-based learning system method.At the same time,the pseudo-inverse solution regression parameters in broad learning are optimized by applying to a Harris Eagle optimization algorithm based on whale algorithm and simulated annealing algorithm.Simulation results show that the proposed method has certain advantages in accuracy and training speed.
Keywords：unmanned surface vesselbroad learning systempose predictionvariational mode decompositionHarris Hawks optimizationwhale optimization algorithm
Publication Date：2026-02-28
Online Publishing Date：2026-04-08(First online date of this platform, not the publication date of the document)
Pages：13( 335-347 )