可调节的基于错误的自适应神经网络跟踪控制不确定的非线性系统
IEEE transactions on cybernetics
|March 5, 2026
概括
这项研究引入了一个可调节错误的神经网络 (NN),用于自适应的神经控制. 这项创新提高了近似精度和跟踪误差在不确定的非线性系统的趋同.
科学领域:
- 控制工程 控制工程 控制工程
- 人工智能的人工智能
- 非线性系统分析 非线性系统分析
背景情况:
- 传统的神经网络 (NN) 控制与可调节的近似误差作斗争.
- 这种限制会影响不确定的非线性系统中的精度和跟踪精度.
- 现有的方法在管理NN近似值和未知函数之间的错误方面缺乏灵活性.
研究的目的:
- 开发一个可调节错误的神经网络 (NN) 近似器.
- 将这个近似器集成到适应性神经跟踪控制器中,用于不确定的非线性系统.
- 为了提高近似精度和跟踪错误的趋同.
主要方法:
- 设计了一个可调节的误差NN近似仪,参数可调.
- 整合了近似器到一个自适应的神经跟踪控制器.
- 利用利亚普诺夫稳定理论进行系统分析和跟踪错误的趋同.
主要成果:
- 与传统方法相比,实现了更高的跟踪错误准确度.
- 证明了对未知非线性函数的近似精度的提高.
- 验证了闭环系统稳定性和跟踪错误的趋同.
结论:
- 拟议的可调节错误的NN近似器增强了自适应神经控制.
- 新的控制器设计在不确定的非线性系统中提供了卓越的性能.
- 模拟和实验结果验证了拟议方案的有效性.
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