实时自适应概率反复的Takagi-Sugeno-Kang模糊神经网络对非线性系统的比例-积分-导数控制器
A Aziz Khater1, Eslam M Gaballah1, Mohammad El-Bardin1
1Department of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menoufia University, Menof 32852, Egypt.
本研究介绍了一种适应性模糊神经PID控制器,可以有效地管理非线性系统中的不确定性,而不需要数学模型. 它提高了工程应用的控制性能和稳定性.
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
- 非线性系统动态 非线性系统动态
背景情况:
- 非线性系统由于固有的不确定性而存在重大挑战.
- 传统的控制器经常与随机不确定性作斗争,需要准确的系统模型.
- 适应性控制策略对于在动态环境中强大的性能至关重要.
研究的目的:
- 开发一个自适应的概率反复的Takagi-Sugeno-Kang模糊的神经PID控制器.
- 在不依赖数学模型的情况下解决非线性系统中的随机不确定性.
- 为了提高控制器的性能和确保系统的稳定性.
主要方法:
- 概率处理与塔卡吉-苏杰诺-康模糊神经系统的整合.
- 利用Lyapunov函数进行自适应参数调整和稳定性保证.
- 调整控制器概率参数以提高控制精度.
主要成果:
- 拟议的控制器有效地处理外部干扰,随机噪音和系统不确定性.
- 与现有控制器相比,在非线性动态工厂中表现出卓越的性能.
- 在工程领域通过模拟和实验验证实适用性.
结论:
- 适应性概率反复的Takagi-Sugeno-Kang模糊神经PID控制器为不确定的非线性系统提供了强大的解决方案.
- 无模型的性质和适应能力使其非常适合于实际的工程应用.
- 在存在各种不确定性的情况下,控制器显著提高了系统的稳定性和性能.
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