水下无人系统学报2023，Vol.31Issue(6)：827-838,12.DOI:10.11993/j.issn.2096-3920.2022-0074
基于ESO的水下机器人机械臂系统鲁棒模型预测控制
ESO-Based Robust Model Predictive Control for Undersea Vehicle Manipulator System
摘要
Abstract
In view of the complexity and uncertainty of the marine environment and the strong nonlinearity and coupling of the undersea vehicle manipulator system(UVMS),this paper proposed a robust model predictive control(RMPC)method based on extended state observer(ESO).First,a dynamics model was established based on the dynamics characteristics of UVMS,and a nominal model system was defined by ignoring uncertainties and disturbances.Then,a UVMS algorithm was designed for the nominal system.The uncertainties,disturbances,modeling errors,and other influencing factors of the original system were summarized into extended states,and an ESO was designed to estimate these factors.Furthermore,the factors were compensated based on the RMPC of the nominal model,so as to obtain the RMPC method applied to the UVMS system.Finally,it is demonstrated through simulation experiments that the ESO-based RMPC has good trajectory tracking performance and anti-disturbance capability.关键词
水下机器人机械臂系统/鲁棒模型预测控制/扩张状态观测器/轨迹跟踪Key words
undersea vehicle manipulator system/robust model predictive control/extended state observer/trajectory tracking分类
军事科技引用本文复制引用
王红都,高枫,黎明,付东飞..基于ESO的水下机器人机械臂系统鲁棒模型预测控制[J].水下无人系统学报,2023,31(6):827-838,12.基金项目
山东省自然科学基金(ZR2021MF119) （ZR2021MF119）
河南省水下智能装备重点实验室开放基金. （）
