非线性多代理系统的基于观察者的容错有限时间控制
概括
本研究介绍了适应性神经控制与执行器故障的非线性多元代理系统. 拟议的方法确保追随者在有限的时间内到达领导者的凸船体,提高系统的稳定性和性能.
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:
- 多代理系统需要强大的控制策略,特别是在执行器故障时.
- 估计未测量状态对于复杂系统的有效控制至关重要.
研究的目的:
- 为具有执行器故障的非线性多元代理系统开发适应性神经控制控制.
- 为了提高系统性能和减少计算负载.
主要方法:
- 通过神经适应观察者利用神经网络进行状态估计.
- 设计一个事件触发的控制规律,以尽量减少计算负担.
- 运用利亚普诺夫稳定理论进行系统分析.
主要成果:
- 闭环系统实现了合作性的半全球统一终极界限性 (CSGUUB).
- 追随者的输出成功地到达由领导者形成的凸船体.
- 控制错误在有限的时间内受到限制.
结论:
- 提出的神经适应控制方案有效地解决了非线性多剂系统中的执行器故障.
- 事件触发方法提供了计算效率.
- 实现了有限时间的制,验证了控制策略.
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