适应性形成控制,以避免不对称输入和度的USVs的障碍
Hu Yancai1, Liu Yang2, Zhang Yan3
1Navigation College, Shandong Jiaotong University, Weihai, China. yancaihu@126.com.
Scientific reports
|September 28, 2024
概括
本研究介绍了一种新的神经网络 (NN) 适应性形成控制方法,用于无人驾驶水面船只 (USV) 安全导航. 该方法提高了在复杂的海上环境中避免障碍物和形成稳定性.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 海洋工程 海洋工程
- 控制系统 控制系统
背景情况:
- 无人驾驶水面船 (USV) 在海上航行中面临复杂的非线性动态.
- 同时保持形成控制和避开障碍是具有挑战性的.
- 外部干扰和内部不确定性会影响USV的表现.
研究的目的:
- 为多个USVs提出一个神经网络 (NNs) 适应形成人工潜力场 (APF) 避障控制方法.
- 解决未知的非线性动态,提高在具有挑战性的海上环境中的安全.
- 为了确保强大的形成控制和避开障碍.
主要方法:
- 使用的辐射基函数 (RBF) NNs用于未知非线性动态的在线近似.
- 实施了领导跟随者控制策略,以评估风险和避免障碍.
- 包含一个不对称的辅助控制系统来处理控制器输入限制和故障.
- 应用了利亚普诺夫稳定定理来保证系统的稳定性.
主要成果:
- 通过使用 RBF NNs 成功近似复杂的非线性动力学.
- 在强风和波浪条件下对多个USV进行有效的风险评估和障碍回避.
- 通过模拟验证了形成控制和避障算法的稳定性.
结论:
- 拟议的NN自适应形成APF方法提高了USV导航安全和形成控制.
- 该方法有效地减轻了非线性动态和环境干扰所带来的挑战.
- 模拟结果证实了算法的有效性和稳定性.
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