为非线性批处理过程设计强大的模糊代学习控制
Wei Zou1, Yanxia Shen1, Lei Wang2
1Engineering Research Center of Internet of Things Technology Applications, Ministry of Education, Jiangnan University, Wuxi 214122, China.
Mathematical biosciences and engineering : MBE
|December 5, 2023
概括
一个新的复合模糊代学习控制 (ILC) 方案使用2D模糊模型稳定非线性批处理过程. 这种强大的控制方法确保了通过模拟验证的稳定性和性能.
科学领域:
- 控制工程 控制工程 控制工程
- 模糊系统 (Fuzzy Systems) 是一个模糊系统.
- 非线性系统是非线性系统.
背景情况:
- 非线性批处理过程由于其复杂的动态和干扰而存在控制挑战.
- 代学习控制 (ILC) 对于重复的任务是有效的,但需要适应非线性系统.
- 模糊系统为建模和控制非线性不确定性提供了强大的框架.
研究的目的:
- 为非线性批处理提出一个二维 (2D) 复合模糊代学习控制 (ILC) 方案.
- 为了解决非重复性干扰,并确保强大的非对称稳定性和2D $H_\infty$性能.
- 开发基于线性矩阵不等式 (LMIs) 的控制器设计方法.
主要方法:
- 通过局部部门非线性方法,使用2D不确定的Takagi-Sugeno (T-S) 模糊模型表示非线性批处理过程.
- 在开发的模糊模型下,将反控制与ILC方案集成.
- 使用利亚普诺夫函数和矩阵转换,建立足够的稳定性和性能条件.
主要成果:
- 获得了足够的条件,以实现强大的非对称稳定性和闭环模糊系统的2D $H_\infty$性能.
- 通过解决一组线性矩阵不等式 (LMIs) 来获得控制器收益.
- 在三系统和连续式反应堆 (CSTR) 上的模拟证明了该方法的可行性和效率.
结论:
- 拟议的2D复合模糊ILC方案有效控制非线性批处理过程与非重复性干扰.
- 该方法保证了强大的稳定性,并实现了所需的性能水平.
- 基于LMI的设计为控制器合成提供了一个实用的方法.
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