通过灰狼优化器进行强大的最佳控制,用于水下车辆操纵系统
Yong Dai1,2, Duo Wang1, Fangyu Shen3
1School of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang, China.
PloS one
|November 29, 2023
概括
本研究介绍了使用灰狼优化器 (GWO) 的水下车辆操纵系统 (UVMS) 的强大最佳控制方法. 这种方法增强了稳定性和干扰排斥,防止模拟不确定性和水干扰.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统工程 控制系统工程
- 海洋工程 海洋工程
背景情况:
- 水下车辆操纵系统 (UVMS) 对于水下操作至关重要.
- 由于建模不确定性和环境干扰,UVMS控制具有挑战性.
- 现有的控制方案需要强大的解决方案,以确保可靠的性能.
研究的目的:
- 为UVMS提出一个强大的最佳控制方法.
- 为了提高UVMS的稳定性和干扰排斥能力.
- 解决在一次性干扰下运行的UVMS中的非线性控制问题.
主要方法:
- 开发了UVMS的非线性动态模型.
- 在一次性干扰下将非线性模型推导为线性状态空间模型.
- 使用灰狼优化器 (GWO) 优化了H∞控制器参数.
- 将GWO与群优化 (ACO),遗传算法 (GA) 和粒子群优化 (PSO) 进行比较.
- 使用硬件在循环 (HIL) UVMS平台验证了控制方案.
主要成果:
- 拟议的GWO优化的H∞控制器表现出更好的性能.
- 与其他算法相比,实现了增强的稳定性和优越的干扰排斥.
- UVMS 显示出强大的抗模拟不确定性和外部水干扰的稳定性.
- HIL的实验证实了拟议的控制方案的有效性.
结论:
- 优化GWO的H∞控制方法为UVMS控制提供了强大的解决方案.
- 该方法有效地减轻了建模不确定性和外部干扰的影响.
- 硬件在循环中的实验验证实了拟议方案的实际适用性和性能.
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