高速载荷入水前需要实现合成攻角的瞬时控制，而高速载荷瞬时姿态控制参数是决定高速载荷能否在合理的合成攻角范围内入水的关键。因此，针对高速载荷瞬时姿态控制参数的整定与优化，本文设计一种改进的粒子群优化算法，设计惯性权重自适应调整方式，使不同粒子根据自身搜索效果产生不同的惯性权重，并且引入遗传算法的思想改善粒子群的搜索能力，从而提高粒子群优化算法的寻优能力，得到恶劣初始条件下高速载荷瞬时姿态控制较高的寻优成功率。
The instantaneous control of the synthetic angle of attack needs to be realized before the high-speed load enters the water, and the instantaneous attitude control parameter of the high-speed load is the key to determining whether the high-speed load can enter the water within a reasonable synthetic angle of attack. Therefore, aiming at the tuning and optimization of instantaneous attitude control parameters of high-speed loads, an improved particle swarm optimization algorithm is designed, which designs the adaptive adjustment method of inertial weights, so that different particles produce different inertia weights according to their own search effects, and the idea of genetic algorithm is introduced to expand the search range of particle swarms, so as to improve the optimization ability of particle swarm optimization algorithms, and obtain a high optimization success rate of instantaneous attitude control of high-speed loads under harsh initial conditions.
2024,46(11): 58-62 收稿日期：2023-07-10
DOI：10.3404/j.issn.1672-7649.2024.11.011
分类号：TP18
作者简介：樊力维(1998-),男,硕士研究生,研究方向为水下航行器控制与导航技术
参考文献：
[1] 智慧. 群智能优化算法的研究及应用[D]. 西安：西安电子科技大学, 2020.
[2] 高岳林, 杨钦文, 王晓峰, 等. 新型群体智能优化算法综述[J]. 郑州大学学报（工学版）, 2022, 43(3): 21-30.
[3] 安家乐, 刘晓楠, 何明, 等. 量子群智能优化算法综述[J]. 计算机工程与应用, 2022, 58(7): 31-42.
[4] 张九龙, 王晓峰, 芦磊, 等. 若干新型智能优化算法对比分析研究[J]. 计算机科学与探索, 2022, 16(1): 88-105.
[5] 韩礼红. 影响力最大化智能优化算法及应用研究[D]. 兰州：兰州大学, 2021.
[6] HOLLAND J H. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence[M]. 2nd ed. Cambridge: MIT Press, 1992.
[7] KENNEDY J, EBERHART R. Particle swarm optimization[C]// IEEE International Conference on Neural Network. New Jersey, USA: IEEE, 1995, (4): 1942-1948.
[8] 杨博雯. 粒子群优化算法的惯性权重研究[D]. 锦州：渤海大学, 2021.
[9] 李会荣, 彭娇. 带有非线性惯性权重和柯西变异的粒子群优化算法[J]. 计算机与数字工程, 2021, 49(7): 1325-1329+1362.
[10] 王生亮, 刘根友. 一种非线性动态自适应惯性权重PSO算法[J]. 计算机仿真, 2021, 38(4): 249-253+451.
