基于概率模糊神经网络的动态系统的间接自适应控制框架
A Aziz Khater1, Eslam M Gaballah1, Mohammad El-Bardini1
1Department of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menoufia University, Egypt.
这项研究引入了一个概率性的Takagi-Sugeno-Kang模糊神经网络 (PTSK-FNN) 用于自适应控制. 这种新的方法通过管理不确定性和确保稳定性来提高PID控制器的性能,优于现有方法.
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
- 模糊系统 (Fuzzy Systems) 是一个模糊系统.
背景情况:
- 适应性控制系统需要强大的方法来处理系统的不确定性和干扰.
- 比率-整数-导数 (PID) 控制器被广泛使用,但可以与复杂的非线性动态作斗争.
- 模糊神经网络提供了一个强大的建模和控制框架,但整合概率处理提高了他们的能力.
研究的目的:
- 为了介绍一个概率的Takagi-Sugeno-Kang模糊神经网络 (PTSK-FNN) 的间接适应性控制.
- 开发基于利亚普诺夫定理的新型在线学习算法,以保证系统稳定性.
- 改进系统识别,以准确计算控制信号,并提高PID控制器的性能.
主要方法:
- 使用维纳模型与PTSK-FNN用于线性和非线性动态的系统识别.
- 动态修改 PTSK-FNN 结构和参数以更新 PID 控制器收益.
- 在TSK模糊神经系统中实施概率方法,以管理混乱的不确定性.
主要成果:
- 拟议的基于PTSK-FNN的自适应控制器在减轻噪音,干扰和不确定性方面明显优于现有控制器.
- 在模拟中实现了平均绝对误差减少34.2%,在实验结果中减少38.6%.
- 通过模拟和实验验证,在非线性动态系统中表现出卓越的性能.
结论:
- 概率性的Takagi-Sugeno-Kang模糊神经网络为间接适应性控制提供了一个可靠的框架.
- 开发的自适应控制策略有效地提高了非线性系统的PID控制器性能.
- 这种方法为工程应用提供了强大的解决方案,需要在不确定的条件下精确控制.
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