自组织特征选择模糊的神经网络为基础的终端滑动模式控制不确定的非线性系统
Yundi Chu1, Cheng Zhou1, Shixi Hou1
1College of Artificial Intelligence and Automation and Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology, Hohai University, Nanjing 210098, China.
ISA transactions
|September 19, 2024
概括
本研究介绍了用于不确定非线性系统 (UNS) 的复合终端滑动模式控制器 (CTSMC). 它使用模糊神经网络 (FNN) 来学习未知的参数,提高控制性能和稳定性.
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
- 非线性系统是非线性系统.
背景情况:
- 不确定非线性系统 (UNS) 由于未知或无法测量的参数,存在重大控制挑战.
- 传统的控制器经常与参数变化作斗争,影响性能和稳定性.
- 滑动模式控制 (SMC) 提供了稳定性,但可能对参数不确定性敏感.
研究的目的:
- 为不确定的非线性系统 (UNS) 开发一个先进的复合终端滑动模式控制器 (CTSMC).
- 通过整合用于自适应参数学习的新型模糊神经网络 (FNN) 来提高CTSMC的控制性能.
- 为了应对现实世界UNS应用中不可测量的系统参数的挑战.
主要方法:
- 证明已知参数的UNS的CTSMC的稳定性和趋同性.
- 开发一个自组织特征选择模糊神经网络 (SOFSFNN),以近似未知的系统参数.
- 将SOFSFNN与CTSMC集成在一起,以适应性控制UNS.
主要成果:
- 拟议的CTSMC与SOFSFNN在不确定的非线性系统中实现了最小的跟踪误差.
- 控制器在系统不确定性和参数变化方面表现出显著的稳定性.
- 该SOFSFNN展示了一个动态调整其网络结构以改善学习的能力.
结论:
- 由SOFSFNN增强的开发的CTSMC为控制不确定的非线性系统提供了强大而有效的解决方案.
- 通过SOFSFNN通过未知的参数进行自适应学习对于实现高控制性能至关重要.
- 控制器的动态网络修改能力在复杂的现实场景中提供了适应性.
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