基于多辨识模型优化切换的USV航向动态反馈控制

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

为了解决无人水面航行器(USV)受外界干扰大且航向控制模型参数未知等航向控制中的相关难题,提出基于多辨识模型切换的动态反馈控制法.此控制法首先根据最小二乘法对USV系统航向模型进行辨识,构建过渡模型集;然后通过预设平均拟合偏差阈值法筛选过渡模型集,构建临时模型集,以避免模型集中子模型过分庞大造成控制过程计算量大的问题;最后根据临时模型集构建动态反馈控制库.为了从控制库中获取"最佳"控制策略,提出以控制性能指标为事件驱动的多辨识模型切换策略.多次湖泊试验表明:基于多模型切换的动态反馈法实现了系统航向无超调、无静差运动,从而提高了USV系统控制品质.

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.