基于切换视线法的欠驱动无人艇鲁棒自适应路径跟踪控制

doi:10.3969/j.issn.1000-1093.2018.12.017

兵工学报 ›› 2018, Vol. 39 ›› Issue (12): 2427-2437.doi: 10.3969/j.issn.1000-1093.2018.12.017

基于切换视线法的欠驱动无人艇鲁棒自适应路径跟踪控制

曾江峰^1,2，万磊^1,2，李岳明^1,2，张英浩^1,2，张子洋^1,2，陈国防^1,2

(1.哈尔滨工程大学水下机器人技术重点实验室，黑龙江哈尔滨 150001;2.哈尔滨工程大学船舶工程学院，黑龙江哈尔滨 150001)

收稿日期:2018-04-04 修回日期:2018-04-04 上线日期:2019-01-31
通讯作者: 李岳明(1983—), 男, 讲师 E-mail:liyueming@hrbeu.edu.cn
作者简介:曾江峰(1989—), 男, 博士研究生。 E-mail: zengjiangfeng@yeah.net;
万磊(1964—) , 男，研究员，博士生导师。 E-mail: wanlei@hrbeu.edu.cn
基金资助:
国家自然科学基金项目（51509057、51509054、51709214）；中央高校基本科研业务费专项资金项目（HEUCF180102）

Switching-line-of-sight-guidance-based Robust Adaptive Path-following Control for Underactuated Unmanned Surface Vehicles

ZENG Jiang-feng^1,2， WAN Lei^1,2， LI Yue-ming^1,2， ZHANG Ying-hao^1,2， ZHANG Zi-yang^1,2， CHEN Guo-fang^1,2

(1.Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering University, Harbin 150001, Heilongjiang, China; 2.College of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, Heilongjiang, China)

Received:2018-04-04 Revised:2018-04-04 Online:2019-01-31

摘要/Abstract

摘要： 针对欠驱动无人艇（USV）的航路点路径跟踪问题，提出了一种鲁棒自适应控制方法。设计了一个新的切换型视线（LOS）法制导律，以引导USV始终以最佳LOS圆半径趋向期望路径。与传统制导律相比，切换型LOS法制导律具有运算负载小、收敛速度快的特点。考虑到USV模型的不确定性及环境干扰未知时变性，开发了一种复合神经网络控制器来增强系统鲁棒性。通过在网络输入中增加预测误差信息，以提高网络逼近精度。同时，引入最小学习参数思想优化网络结构，对神经网络的在线自适应参数进行压缩，以减轻网络计算负担，并借助李雅普诺夫理论对系统的稳定性进行了分析。通过对比仿真实验验证了所提出控制策略的有效性。

关键词: 欠驱动无人艇, 路径跟踪, 自适应控制, 神经网络

Abstract: A robust adaptive control method is proposed for the way-point-based path following control of underactuated unmanned surface vehicles (USVs). A switching line-of-sight (LOS) guidance law is designed to keep USV always running to a desired path with the best LOS circle radius. Switching LOS guidance law has the advantages of less computing burden and faster convergence compared to the traditional guidance methods. Considering the system uncertainties of vehicle and the unknown time-varying disturbances, a composite neural network controller is developed to enhance the robustness of the system. The approximation accuracy of network is improved by adding the predicted error information in the network input. The number of online adaptive parameters is reduced, which effectively lightens the computing burden, by using the minimal learning parameter techniques to optimize the network structure. The stability of system is analyzed based on the Lyapunov theory. The simulated results are presented to demonstrate the effectiveness of the proposed control strategy. Key

Key words: underactuatedunmannedsurfacevehicle, pathfollowing, adaptivecontrol, neuralnetwork

中图分类号:

TP273⁺.21

曾江峰，万磊，李岳明，张英浩，张子洋，陈国防. 基于切换视线法的欠驱动无人艇鲁棒自适应路径跟踪控制[J]. 兵工学报, 2018, 39(12): 2427-2437.

ZENG Jiang-feng， WAN Lei， LI Yue-ming， ZHANG Ying-hao， ZHANG Zi-yang， CHEN Guo-fang. Switching-line-of-sight-guidance-based Robust Adaptive Path-following Control for Underactuated Unmanned Surface Vehicles[J]. Acta Armamentarii, 2018, 39(12): 2427-2437.

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基于切换视线法的欠驱动无人艇鲁棒自适应路径跟踪控制

Switching-line-of-sight-guidance-based Robust Adaptive Path-following Control for Underactuated Unmanned Surface Vehicles

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