自建切比舍夫模糊神经补充滑动模式控制及其应用
IEEE transactions on neural networks and learning systems
|January 10, 2024
概括
对于主动功率过器 (APF) 的一种新的控制策略使用自构造的切比舍夫模糊循环神经网络 (SCCFRNN) 来有效地抑制波. 这种先进的方法提高了建模精度,并减少了控制负担,以获得更好的电源质量.
科学领域:
- 电气工程 电气工程
- 控制系统 控制系统
- 人工智能的人工智能
背景情况:
- 主动功率过器 (APF) 对于减轻电力系统中的波扭曲至关重要.
- 对APF中非线性动态的准确建模对于有效的和声抑制至关重要.
- 现有的控制方法可能面临模型不确定性和计算复杂性的挑战.
研究的目的:
- 提出一种新的互补滑动模式 (CSM) 控制器,与自构造的切比舍夫模糊反复神经网络 (SCCFRNN) 集成,用于APF波抑制.
- 通过使用SCCFRNNN.通过近似未知非线性项来提高APF动态的建模精度.
- 通过智能近似来减少CSM控制器 (CSMC) 的控制负担.
主要方法:
- 开发一个能够通过自学算法自动结构学习的SCCFRNN.
- 将SCCFRNN与CSM控制器集成,用于APF中的声抑制.
- 使用适应性定律来实时调整SCCFRNN参数.
- 利用模糊神经网络 (FNN),循环神经网络 (RNN) 和切比舍夫神经网络 (CNN) 的综合优势.
主要成果:
- 在APF模型中,SCCFRNN有效地近似了未知的非线性动态.
- 拟议的CSM控制器与SCCFRNN展示了卓越的声抑制能力.
- 模拟和硬件实验验证控制策略的可行性和有效性.
- 基于SCCFRNN的方法在精度和控制复杂性降低方面比传统方法具有优势.
结论:
- 拟议的基于SCCFRNN的CSM控制器是APF中波抑制的可行和优质解决方案.
- SCCFRNN的自建性质提高了适应性,并减少了需要精确的先前系统知识的需求.
- 这种智能控制方法有助于提高电源质量和系统效率.
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