在完全不确定的情况下,适应性形成学习控制用于合作型AUV
Emadodin Jandaghi1, Mingxi Zhou2, Paolo Stegagno3
1Department of Mechanical, Industrial and Systems Engineering, University of Rhode Island, Kingston, RI, United States.
Frontiers in robotics and AI
|March 3, 2025
概括
本研究介绍了自动水下车辆 (AUV) 的自适应性形成控制,不需要系统动态知识. 新的框架确保稳定和准确的控制,尽管未知的参数和环境干扰.
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:
- 适应性形成控制对于自动水下车辆 (AUV) 至关重要,但通常依赖于对系统动态或环境数据的部分知识.
- 现有的方法依赖于诸如已知的质量矩阵等假设,这限制了它们在各种水下环境中的适应性.
研究的目的:
- 为自动驾驶汽车开发一个配置无关的自适应性形成控制框架,在不需要对系统动态或环境条件的预先了解的情况下运行.
- 提高AUV在复杂的水下场景中的适应性和强度.
主要方法:
- 建议采用两层框架:一个用于代理间通信的合作估计器和一个用于轨迹控制的去中心化决定性学习 (DDL) 控制器.
- 辐射基函数神经网络 (RBFNN) 在框架内用于存储动态信息,避免在系统重启后需要重新学习.
- 该系统将所有动态,包括质量矩阵,视为完全未知,确保广泛适用.
主要成果:
- 该框架有效地解决了来自未知的参数,未建模的相互作用和外部干扰的不确定性,例如变化的电流和压力.
- 内部机制处理参数不确定性和未建模的相互作用,而控制器管理外部环境因素.
- 这种方法表明,在各种水下环境中增强了适应能力.
结论:
- 数学证明证实了拟议的控制器的稳定性.
- 模拟结果验证了框架能够为每个代理实现精确的控制准确性和信号界限性的能力.
- 该研究证实了框架在复杂场景中的稳定性和弹性,为AUV形成控制提供了强大的解决方案.
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