Dynamic Feedback Controller Based on Optimized Switching of Multiple Identification Models for Course Control of Unmanned Surface Vehicle

A dynamic feedback control algorithm based on multiple identification models switching is proposed to solve the problems in course control of the unmanned surface vehicle (USV), such as large external disturbances and unknown course control model parameters. Firstly, a transitional model set is constructed by identifying USV course model according to the least square method. Then, a temporal model set is selected from the transitional models by introducing mean fitting error, which can avoid huge computation cost caused by large amount of sub-models in model set. Finally, a dynamic feedback controller database is designed based on the temporal model set. Meanwhile, using control performance indexes as event driven factors, some switching methods for multiple identification models are taken to obtain optimal controllers from the controller database. Some lake trials show that the multi-model switching based dynamic feedback method can improve the control performances, and the USV can move without overshoots or static errors.