为解决欠驱动自主式水下航行器的定深控制问题，建立欠驱动自主式水下航行器的数学模型，选用经典的PID控制器对其进行控制。为使控制器的各项性能指标良好，控制器的参数整定选用粒子群优化算法。粒子群算法在迭代过程中容易出现粒子早熟现象，为了避免这一现象，本文引入指数函数，对粒子群迭代公式的惯性权重进行动态调整，延长了粒子的大范围搜索时间。在Matlab 2019b环境下进行仿真，通过纵向对比，证明了改进算法的可行性，将改进后的算法与ZN整定算法进行对比，结果表明，改进粒子群算法表现更佳。
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.
2022,44(8): 64-68 收稿日期：2021-06-07
DOI：10.3404/j.issn.1672-7649.2022.08.013
分类号：U661.3
基金项目：国家重点研发计划资助项目（2017YFC0305802）；浙江省重点研发计划资助项目（2021C03186）
作者简介：罗建超(1995-),男,硕士研究生,研究方向为自主式水下航行器控制
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