在PDE模型下的非线性多代理系统的有限时间共识自适应神经网络控制
IEEE transactions on neural networks and learning systems
|April 22, 2024
概括
使用神经网络的新适应性控制方法可以帮助多代理系统 (MAS) 在有限的时间内达成共识,即使存在非线性和干扰. 这种方法确保了系统的稳定性和有效的协调.
科学领域:
- 控制理论 控制理论
- 人工智能的人工智能
- 系统工程 系统工程
背景情况:
- 多代理系统 (MAS) 经常面临非线性动态和外部干扰的挑战,阻碍了协调的行为.
- 在MAS中达成共识对于机器人,分布式计算和传感器网络等应用至关重要.
- 现有的控制方法可能难以应对非线性MAS的复杂性和不可预测的环境因素.
研究的目的:
- 为具有非线性函数和外部干扰的多代理系统 (MAS) 提出一种新的自适应控制方法.
- 为了利用神经网络近似能力来建模复杂的MAS动态.
- 在具有挑战性的操作条件下,在MAS中实现有限时间的共识.
主要方法:
- 利用神经网络的近似特性来建模两个变量非线性项的 MAS 部分微分方程 (PDE).
- 设计了一个自适应控制器,以驱动抛物线MAS向共识,尽管外部干扰.
- 应用了有限时间定理和特殊的不等式,以严格证明闭环系统的稳定性.
主要成果:
- 成功开发了一种适应性控制策略,用于具有外部干扰的非线性MAS.
- 证明了拟议方法能够在有限时间内达成共识的能力.
- 通过全面的数值模拟验证了基于神经网络的自适应控制的有效性.
结论:
- 拟议的基于神经网络的自适应控制方法可以在具有非线性和外部干扰的多代理系统 (MAS) 中实现有限时间共识.
- 闭环系统的稳定性是用有限时间定理和不等式数学证明的.
- 数字模拟证实了开发的控制策略的实际有效性和稳定性.
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