实时海洋电流补偿AUV轨迹跟踪控制使用超学习和自我适应混合方法
Yiqiang Zhang1, Jiaxing Che2, Yijun Hu3
1College of Computer Science and Technology, Jilin University, Changchun 130012, China.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
这项研究引入了一种混合方法,用于自动水下车辆 (AUV) 适应海洋流. 该方法结合了深度神经网络和自适应控制,以提高轨迹跟踪性能.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 海洋学 海洋学 海洋学
- 控制系统工程 控制系统工程
背景情况:
- 自主水下车辆 (AUV) 由于复杂的水力动力学效应而面临海流轨道偏差.
- 传统的基于模型的控制方法与时间变化的参数和不准确的数学模型作斗争.
研究的目的:
- 为AUV开发适应性控制策略,以有效地导航水下电流.
- 通过解决传统控制方法的局限性来提高AUV跟踪性能.
主要方法:
- 建议采用混合的超级学习和自我适应方法.
- 一个深度神经网络 (DNN) 在线学习一个高阶的水力动力学模型.
- 一个自适应法动态调整线性系数在线实时推力补偿.
主要成果:
- 与模拟中的替代控制器相比,混合方法显著提高了AUV跟踪性能.
- 拟议的方法证明了AUV在适应不同海洋电流速度方面的熟练程度.
- 该组合利用了DNN的表示能力和自适应控制的快速响应.
结论:
- 开发的混合控制策略使AUV能够有效地适应动态的海洋流.
- 这种方法提供了一个强大的解决方案,用于改善在具有挑战性的海洋环境中AUV导航和控制.
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