适应神经网络 代学习 PI 控制分数顺序非线性系统使用通用屏障莱普诺夫函数
IEEE transactions on cybernetics
|December 2, 2025
概括
本研究介绍了一种适应神经网络 (ANN) 控制器,用于具有约束的分数顺序非线性系统 (FONS). 它放松了屏障莱普诺夫函数 (BLF) 设计条件,确保了系统稳定性和约束满足.
科学领域:
- 控制理论 控制理论
- 非线性系统是非线性系统.
- 机器学习 机器学习
背景情况:
- 传统的屏障莱普诺夫函数 (BLF) 设计需要光滑凸起的函数,限制了适用性.
- 具有全态约束的分数顺序非线性系统 (FONS) 存在重大控制挑战.
研究的目的:
- 为FONSs开发一种基于比例积分 (PI) 的代学习跟踪控制方法的新型自适应神经网络 (ANN).
- 克服现有的BLF设计的限制性先决条件.
- 确保系统稳定性和遵守全状态约束.
主要方法:
- 提出了一种新的BLF类型,它只需要一个单调的衍生品,在分数Lyapunov直接方法下.
- 一个自适应神经网络 (ANN) PI代学习跟踪控制器是使用后退方法设计的.
- 控制器结合了恒定增益和动态变量,以减少计算复杂性.
主要成果:
- 所有FONS的闭环信号都被证明是半全球最终均的边界.
- 拟议的方法有效地确保不违反系统约束.
- 代学习算法促进了连续或不连续的自我学习和更新.
结论:
- 开发的ANN PI代学习控制策略为具有约束力的FONS提供了更灵活和更有效的方法.
- 理论分析和数值模拟证实了该方法的合理性和性能.
- 这项工作通过放松BLF条件来推进复杂非线性系统的控制设计.
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