水下自主航行器垂直面运动的预测控制

摘要

To address the problem of depth tracking and attitude control of autonomous underwater vehicle ( AUV) near the surface, a novel nonlinear reduced-order state observer ( ROSO ) and a predictive controller based on nonlinear model online linearization ( PC-NMOL) are presented. By using a nonsingular coordinate transformation, the ROSO is achieved to accurately estimate the state variables of AUV. And the state estimation is applied to the predictive controller to enhance the attitude control and depth tracking performance of AUV. In simulation of AUV longitudinal motion control, the comparison has been presented between ROSO and full-order state observer ( FOSO) , also between PC-NMOL and traditional nonlinear predictive control ( NPC) . Simulation results show the fast dynamical response and strong robustness of proposed methods.%针对近水面海浪干扰下水下自主航行器(AUV)的深度跟踪及姿态控制问题,提出一种基于非线性降维状态观测器(ROSO)的预测控制.通过非奇异坐标变换实现AUV垂直面运动非线性模型的状态估计,并将状态估计结果用于预测控制及其预测模型的在线线性化过程中,在仿真实验中对ROSO与全维状态观测器(FOSO)的状态估计结果进行对比,同时也对比了所提出的基于在线线性化的预测控制(PC-NMOL)与现有的非线性预测控制(NPC)的控制效果.仿真结果证明了所提出的方法可以得到精确的状态估计,且具有动态响应快,对外部扰动鲁棒性强的特点.