舰船科学技术  2022, Vol. 44 Issue (8): 64-68    DOI: 10.3404/j.issn.1672-7649.2022.08.013 PDF

1. 上海交通大学，上海 200240;
2. 自然资源部第二海洋研究所，浙江 杭州 310012

Depth control of autonomous underwater vehicle based on improved particle swarm optimization algorithm
LUO Jian-chao1,2, ZHU Xin-ke2
1. Shanghai Jiaotong University, Shanghai 200240, China;
2. Second Institute of Oceanography, Hangzhou 310012, China
Abstract: In order to solve the problem of depth determination control of the underactuated autonomous underwater vehicle, a mathematical model of the underactuated autonomous underwater vehicle was established, and the classical PID controller was used to control the underactuated autonomous underwater vehicle. In order to improve the performance of the controller, the particle swarm optimization algorithm is used to set the parameters of the controller. Particle swarm optimization (PSO) is prone to precocity in the iterative process. In order to avoid this phenomenon, this paper introduces exponential function to dynamically adjust the inertia weight of PSO iterative formula, which prolongs the large-range searching time of particles. The simulation result in Matlab 2019b demonstrated the feasibility of the improved algorithm. The improved algorithm is compared with the ZN tuning algorithm, and the results show that the improved particle swarm optimization algorithm performs better.
Key words: particle swarm optimization     PID controller     autonomous underwater vehicle     inertia weight
0 引　言

1 欠驱动自主水下航行器建模

 ${M}\dot{\mathit{v}}+C\left(\mathit{v}\right)\mathit{v}=\mathit{\tau }+\mathit{\omega } 。$ (1)

 $\left\{\begin{array}{l}\dot{u}=\dfrac{{m}_{2}}{{m}_{1}}vr-\dfrac{{m}_{3}}{{m}_{1}}wq-\dfrac{{d}_{1}}{{m}_{1}}u+\dfrac{1}{{m}_{1}}{F}_{x}+{\omega }_{1}，\\ \dot{v}=-\dfrac{{m}_{1}}{{m}_{2}}ur-\dfrac{{d}_{2}}{{m}_{2}}v+{\omega }_{2}，\\ \dot{w}=\dfrac{{m}_{1}}{{m}_{3}}uq-\dfrac{{d}_{3}}{{m}_{3}}w+{g}_{1}+{\omega }_{3}，\\ \dot{q}=\frac{{m}_{3}-{m}_{1}}{{m}_{5}}uw-\dfrac{{d}_{4}}{{m}_{5}}q-{g}_{2}+\dfrac{1}{{m}_{5}}{b}_{1}{\delta }_{s}+{\omega }_{4}，\\ \dot{r}=\dfrac{{m}_{1}-{m}_{2}}{{m}_{6}}uv-\dfrac{{d}_{5}}{{m}_{6}}r+\dfrac{1}{{m}_{6}}{b}_{2}{\delta }_{r}+{\omega }_{5}。\end{array}\right.$ (2)

2 PID控制器原理

PID控制器的结构简单，控制原理易于理解，具有很好的鲁棒性，其诸多优点使其成为运用最广的控制器之一。

PID控制器的原理如图1所示，由比例、积分、微分3个部分构成。In为目标信号，Out为被控对象的输出，二者的差值e为偏差信号，u为控制器输出。被控对象的输入信号可表示为：

 ${u}\left({t}\right)={K}_{p}e\left(t\right)+{K}_{i}{\int }_{0}^{t}e\left(t\right){\rm{d}}t+{K}_{d}\frac{{\rm{d}}e\left(t\right)}{{\rm{d}}t}。$ (3)