﻿ 基于鲁棒控制器的水面智能高速无人艇控制技术
 舰船科学技术  2023, Vol. 45 Issue (10): 74-77    DOI: 10.3404/j.issn.1672-7649.2023.10.015 PDF

Control technology of high speed intelligent unmanned vehicle based on robust controller
CHEN Xing-li
Jiangsu Shipping College, Nantong 226010, China
Abstract: The parameters in the model will be disturbed due to changes in the speed and load of the hull, and factors such as sea wind, waves, and currents will also interfere with the model. Parameter perturbation, external disturbance and model error lead to the instability of surface intelligent high-speed unmanned vehicle control. Various uncertain factors in ship mathematical modeling have a great impact on the robust performance of ship maneuvering system. This paper mainly establishes the mathematical model of the unmanned boat, and then uses the robust control system to analyze and optimize the parameters of the model, so as to further improve the stability of the entire unmanned boat control system.
Key words: surface intelligent high speed unmanned craft     robust control
0 引　言

1 水面智能高速无人艇概述

2 水面智能高速无人艇鲁棒控制 2.1 无人水面艇鲁棒控制系统方法论分析

2.2 鲁棒控制器在水面智能高速无人艇中的模型分析

 $\Delta P\left( s \right) = P\left( s \right) - {P_0}\left( s \right) \text{，}$ (1)

 $\left| {\Delta P\left( {j\varpi } \right)} \right| < \left| {W\left( {j\varpi } \right)} \right|,\forall \varpi \in \left[ {0,\infty } \right) \text{。}$ (2)

 图 1 具有加法不确定性的控制系统 Fig. 1 Control system with additive uncertainty

 $Tz\omega \left( s \right) = - \frac{{C\left( s \right)}}{{1 + {P_0}\left( s \right)C\left( s \right)}} \text{。}$ (3)

 $\left| {\Delta P\left( {j\varpi } \right)} \right| < \left| {W\left( {j\varpi } \right)} \right| < 1,\forall \varpi \in R \text{。}$ (4)

 $\left|\frac{C\left(j\varpi \right)}{1+{P}_{0}\left(j\varpi \right)C\left(j\varpi \right)}W\left(j\varpi \right)\right| < 1，\forall \varpi \in R \text{。}$ (5)

 图 2 基础控制系统 Fig. 2 Basic control system

 ${a_{mn}}\left( \theta \right) = {c_m}\exp \left\{ {j2{\text{π}} \frac{{\left( {m{d_t} + n{d_r}} \right)\sin \theta }}{{{\lambda _m}}}} \right\} \text{。}$ (6)

 $\frac{{{f_m}n{d_r}\sin \theta }}{c} = \frac{{n{d_r}\sin \theta }}{{{\lambda _0}}} \cdot \frac{{{\lambda _0}}}{{{\lambda _m}}} = \frac{{{\varepsilon _m}n{d_r}\sin \theta }}{{{\lambda _0}}} \text{。}$ (7)

${\varepsilon _m} = {{{\lambda _0}} \mathord{\left/ {\vphantom {{{\lambda _0}} {{\lambda _m}}}} \right. } {{\lambda _m}}} = {{{f_m}} \mathord{\left/ {\vphantom {{{f_m}} {{f_0}}}} \right. } {{f_0}}}$ 为控制系统的外部扰动因子，形成了多路的导向矢量为：

 $a = {\left[ {{a_{11}} \cdots {a_{1N}} \cdots {a_{MN}}} \right]^{\rm{T}}}。$
 ${a_{mn}}\left( \theta \right) = {c_m}\exp \left\{ {j2{\text{π}} \frac{{{\varepsilon _m}n{d_r}\sin \theta }}{{{\lambda _0}}}} \right\} \text{。}$ (8)

 ${\kern 1pt} X\left( t \right) = a\left( \theta \right)s\left( t \right) + {N_{mn}}\left( {{f_m}} \right) 。$

 $G = \frac{{{{|{W^H}V{|^2}} \mathord{\left/ {\vphantom {{|{W^H}V{|^2}} {{W^H}W}}} \right. } {{W^H}W}}}}{{{{{W^H}QW} \mathord{\left/ {\vphantom {{{W^H}QW} {{W^H}W}}} \right. } {{W^H}W}}}} \text{，}$ (9)

 ${\cos ^2}\varphi = \frac{{|{W^H}V{|^2}}}{{\left( {{W^H}W} \right)\left( {{V^H}V} \right)}} \text{，}$ (10)

 $G = M{\cos ^2}\varphi \left\{ {\frac{{{W^H}W}}{{{W^H}QW}}} \right\} \text{，}$ (11)

3 无人艇航线跟踪的鲁棒控制器设计

 图 3 鲁棒控制器精度随时间变化曲线 Fig. 3 Robust controller accuracy vs. time variation curve

 图 4 外部扰动分量随时间的变化曲线 Fig. 4 Variation curve of external disturbance component with time

 图 5 流体扰动分量随时间的变化曲线 Fig. 5 Variation curve of fluid disturbance component with time

PID控制器的模拟曲线没有鲁棒控制器那么平滑，因此，PID控制器对波浪干扰的鲁棒性小，但鲁棒控制器对波浪的扰动有很强的抑制作用。其主要原因在于：在鲁棒控制中，既要考虑外部扰动的不确定性，又要考虑外部扰动和流体力学参数的不确定性，同时要兼顾暂态特性、抗扰动性能以及鲁棒性，以增强鲁棒控制的有效性。

4 结　语

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