干扰 基于观察者的神经网络 不单一的固定时间适应性共识控制 不确定的非线性多代理系统
IEEE transactions on cybernetics
|March 10, 2026
概括
本研究介绍了一种用于非线性多代理系统 (MAS) 的新型神经网络 (NN) 适应控制器,以在固定的时间内实现共识,克服参数不确定性和干扰.
科学领域:
- 控制系统工程 控制系统工程
- 人工智能的人工智能
- 非线性动力学是一种非线性动力学.
背景情况:
- 非线性多代理系统 (MAS) 经常面临参数不确定性和外部干扰的挑战.
- 在固定的时间内实现共识控制对于许多现实应用来说至关重要.
- 现有的控制方法可能会遭受奇点问题或缓慢的融合.
研究的目的:
- 为具有参数不确定性的非线性MAS开发一种非单一的固定时间自适应共识控制策略.
- 设计一种新的分布式神经网络 (NN) 适应控制器,有效处理干扰.
- 确保在固定时间间隔内快速稳定地实现跟踪错误的趋同.
主要方法:
- 引入一个基于中间变量的一般化干扰观察器 (IVBDO) 以实时在线估计不匹配的外部干扰.
- 构建一个分布式的固定时间NN自适应控制器,利用一个四进制的Lyapunov函数.
- 在固定时间设计过程中消除奇点现象的控制协议的开发.
主要成果:
- 拟议的控制算法保证了半全球均最终边界 (SGUUB) 系统行为.
- 分布式输出跟踪错误被证明在固定的时间间隔内汇聚到一个可调节的紧集,围绕原点.
- 模拟结果验证了建议的控制方法的有效性和性能.
结论:
- 新的NN自适应控制策略有效地解决了非线性MAS的非单元固定时间共识控制.
- 基于IVBDO和NN控制的集成提供了针对不确定性和干扰的强大性能.
- 该方法确保了快速和稳定的融合,在MAS控制理论和实践中取得了重大进展.
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