基于AUKF-NMPC算法的无人船轨迹跟踪控制  

Trajectory Tracking of USV Based on AUKF-NMPC Algorithm

作　　者：杨智博 张浩晢 焦绪国 雷鹏 朱齐丹 YANG Zhibo;ZHANG Haozhe;JIAO Xuguo;LEI Peng;ZHU Qidan(Qingdao University of Technology,Qingdao 266000,China;Harbin Engineering University,Harbin 150000,China)

机构地区：[1]青岛理工大学,山东青岛266000 [2]哈尔滨工程大学,哈尔滨150000

出　　处：《电光与控制》2024年第12期14-18,40,共6页Electronics Optics & Control

基　　金：国家自然科学基金(62373209,61803220,61573203,62203249);山东省重大创新工程(2022CXGC010608)。

摘　　要：针对复杂外部环境下无人船(USV)的轨迹跟踪问题,提出了一种将自适应无迹卡尔曼滤波(AUKF)与非线性模型预测控制(NMPC)相结合的控制算法。首先,建立三自由度全驱动无人船的数学模型,并设计基于当前跟踪误差动态调整权重的非线性模型预测控制器。其次,根据无人船航行所面对复杂环境扰动及传感器不确定噪声干扰的特点,提出了将噪声变化与量测参数相关联的自适应无迹卡尔曼滤波器并用于状态估计。最后,通过引入时变随机扰动及测量噪声来模拟真实外部环境的仿真实验,验证了该算法的确定性及有效性。To solve the trajectory tracking problem of Unmanned Surface Vessels(USV)in complex external environment,a control algorithm combining Adaptive Unscented Kalman Filter(AUKF)with Nonlinear Model Predictive Control(NMPC)is proposed.Firstly,the mathematical model of the 3-DOF fully-driven USV is established,and a nonlinear model predictive controller with dynamic weight adjustment based on current tracking error is designed.Secondly,according to the characteristics of complex environment and uncertain sensor noise,an AUKF is proposed to estimate the state by correlating noise changes with measurement parameters.Finally,by adding time-varying random disturbance and measurement noise to simulate the real external environment for the numerical simulation experiment,the certainty and effectiveness of the algorithm are verified.

关 键 词：无人船 轨迹跟踪 非线性模型预测控制 自适应无迹卡尔曼滤波 

分 类 号：TP273.2[自动化与计算机技术—检测技术与自动化装置]