对于严格反非线性系统的固定时间命令过复合适应神经故障耐受性控制
Siwen Liu1, Huanqing Wang2, Tieshan Li3
1The Navigation College, Dalian Maritime University, Dalian 116026, China.
ISA transactions
|December 6, 2023
概括
本研究介绍了一种用于非线性系统的新型固定时间命令过复合体自适应神经故障耐受 (FCCANF) 控制器. 控制器有效地处理未知的功能和干扰,确保有界的系统变量和输出收.
科学领域:
- 控制理论 控制理论
- 非线性系统是非线性系统.
- 人工智能的人工智能
背景情况:
- 严格反非线性系统 (SFNSs) 通常包含未知函数和外部干扰.
- 传统的控制方法与SFNS固有的复杂性和不确定性作斗争.
- 耐故障控制对于在组件故障或干扰下保持系统稳定性和性能至关重要.
研究的目的:
- 为SFNSs开发一个固定时间命令过复合材料自适应神经故障耐受 (FCCANF) 控制策略.
- 为了应对未知的系统功能和局限性干扰所带来的挑战.
- 为了确保系统内部变量保持局限,输出在固定的时间内汇聚到零点附近的小间隔.
主要方法:
- 使用辐射基函数神经网络 (RBFNNs) 与串行并行估计模型 (SPEM) 来近似未知函数.
- 实施一种新的固定时间命令波器和适应性干扰观察器,以管理复杂性和补偿外部干扰.
- 设计一种自适应控制技术来创建FCCANF控制器.
主要成果:
- 成功解决了控制设计中的复杂性爆炸问题.
- 有效地补偿外部干扰.
- 证明了系统内部变量是有界的.
- 展示了输出变量在固定的时间内与初始条件独立地趋于零周围的一个小区间.
- 通过数值和实践示例验证了控制技术.
结论:
- 拟议的FCCANF控制技术对具有未知的功能和干扰的严格反非线性系统有效.
- 该方法确保了有限时间的融合和对不确定性的稳定性.
- 这种方法为复杂的非线性系统中的容错控制提供了可行的解决方案.
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