基于自适应神经网络的领导者遵循的共识控制,用于一类二级非线性多代理系统
Fathia Moh Al Samman1, Wajiha Abdul Khaliq2, Shreefa O Hilali3
1Department of Mathematics, College of Sciences, Northern Border University, Arar, Saudi Arabia.
Scientific reports
|October 15, 2025
概括
本研究开发了一种适应性神经网络控制策略,用于面临故障和和的二级非线性多代理系统 (MAS). 这种方法确保了领导者遵循共识,尽管存在复杂的不确定性和失败.
科学领域:
- 控制理论 控制理论
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
背景情况:
- 多代理系统 (MAS) 在各种领域至关重要,但它们的控制受到非线性动态,输入和和组件故障的挑战.
- 现有的控制方法通常依赖于像全球利普希茨条件这样的限制性假设,限制其适用于复杂的现实世界的场景.
研究的目的:
- 在具有实际限制的二级非线性MAS中解决领导者遵循的共识问题.
- 提出一个强大的自适应控制策略,适应未知的非线性动态,输入和,执行器故障和传感器故障.
主要方法:
- 使用微分平均值定理来处理未知的非线性因素,比传统方法更有效.
- 开发了一个基于分布式自适应神经网络 (NN) 的控制器,只使用相对位置和速度数据,避免了对完整状态的测量.
- 采用稳定性理论和利亚普诺夫函数来严格证明领导者遵循共识的实现.
主要成果:
- 拟议的自适应性NN控制策略成功地在二级非线性MAS中实现了领导者遵循的共识.
- 控制器表现出对未知的非线性动力学,执行器故障,传感器故障和输入和的弹性.
- 模拟结果验证了在复杂的MAS中提出的控制方法的有效性和稳定性.
结论:
- 该研究提出了一种新且有效的适应性控制解决方案,用于挑战MASs场景.
- 该方法通过解决多个现实世界的约束来增强共识控制的实际适用性.
- 这些发现有助于推进分布式自主系统的强有力的控制策略.
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