事件触发的自适应神经控制用于MIMO非线性系统,通过命令波器通过速度依赖的歇斯底里和全状态约束
IEEE transactions on cybernetics
|September 25, 2023
概括
本研究介绍了一种适应神经网络控制策略,用于具有歇斯底里的非线性系统,减少通信频率并确保系统稳定性,尽管存在状态约束.
科学领域:
- 控制系统工程 控制系统工程
- 非线性动力学是一种非线性动力学.
- 人工智能的人工智能
背景情况:
- 多输入和多输出 (MIMO) 非线性系统经常表现出复杂的行为,如速度依赖性歇斯底里.
- 国家约束和执行器限制在控制这些系统方面带来了重大挑战.
- 传统的控制方法在触发时刻与控制信号的不可区分性质作斗争.
研究的目的:
- 为MIMO非线性系统开发一个事件触发的自适应神经网络控制方案.
- 为了解决未知的取决于速率的歇斯底里和完整状态约束.
- 为了减少控制器和执行器之间的通信频率,使用事件触发机制.
主要方法:
- 利用自适应神经网络 (NN) 来近似未知的非线性函数.
- 采用命令过技术来管理控制信号的可分性并避免复杂性爆炸.
- 应用屏障 Lyapunov 功能确保系统状态保持在所需区域内.
- 开发了适应性定律,用于更新未知的hysteresis参数.
- 实施了事件触发机制 (ETM) 以尽量减少控制器-执行器通信.
主要成果:
- 成功设计了一个控制系统,可以考虑取决于速率的歇斯底里和状态约束.
- 通过事件触发方法证明了通信频率的减少.
- 通过严格的分析来验证闭环系统的稳定性.
- 通过模拟验证了拟议的控制战略的有效性.
结论:
- 拟议的事件触发的自适应NN控制有效地处理MIMO非线性系统与歇斯底里和状态约束.
- 该方法为减少控制系统中通信负载提供了一个实际的解决方案.
- 这种方法确保了系统稳定性和在存在不确定性的情况下的性能.
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