基于动态事件的自适应性NN输出反控制相互连接的非线性系统在一般输出约束下
Rui Meng1, Changchun Hua2, Kuo Li1
1The institute of Electrical Engineering, Yanshan University, Qinhuangdao, 066004, China.
概括
本研究为非线性系统引入了一种新的动态事件触发控制,确保有界信号和输出约束满足. 适应神经网络 (NN) 方法提高了跟踪控制性能.
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.
- 人工智能在控制中
背景情况:
- 相互连接的非线性系统在自适应控制方面存在挑战,原因是无法测量的状态和输出约束.
- 现有的控制算法通常需要连续的信号传输,导致高通信负担.
研究的目的:
- 为适应神经网络 (NN) 输出反跟踪控制开发一个动态事件触发的输出约束控制算法.
- 通过减少通信频率和处理灵活的约束边界来解决现有方法的局限性.
主要方法:
- 一个减少顺序的动态增益K过器被设计用于估计无法测量的状态.
- 提出了一个不对称的,时间变化的约束函数来管理潜在的无限初始约束边界.
- 使用一个arctangent函数的动态事件触发机制被实现,以最大限度地减少控制信号传输.
主要成果:
- 拟议的控制算法确保了闭环系统中的所有信号仍然受到限制.
- 追踪错误被严格证明满足指定的输出约束.
- 数字模拟验证了开发的控制策略的有效性和性能.
结论:
- 新的动态事件触发控制算法有效地解决了相互连接的非线性系统的自适应性NN输出反跟踪.
- 与传统的控制策略相比,拟议的方法通过减少数据传输和适应动态约束提供了更有效和更强大的解决方案.
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