概括
本研究为未知的非线性系统引入了数据驱动的动态内部模型控制 (D3IMC),消除了对显式模型的需求. 该D3IMC方案有效地处理系统不确定性和干扰,使用自适应控制策略.
科学领域:
- 控制工程 控制工程 控制工程
- 系统识别系统识别系统
- 数据驱动的控制控制数据驱动的控制
背景情况:
- 传统的基于模型的内部模型控制 (IMC) 需要明确的系统模型,这些模型对于复杂的非线性非关联系统来说很难获得.
- 现有的数据驱动方法经常与非线性和非亲缘结构作斗争.
研究的目的:
- 为未知非线性非亲系系统提出一个新的数据驱动动态内部模型控制 (D3IMC) 方案.
- 在控制设计中绕过显式系统建模的需要.
- 加强对模型不确定性和外部干扰的稳定性.
主要方法:
- 通过紧的动态线性化方法使用输入输出数据开发动态内部模型 (DIM).
- 根据D3IMC的建议,使用了名义和不确定性补偿算法.
- 整合了一个适应性参数,更新强度的定律.
- 扩展到基于线性化的全形动态D3IMC,用于复杂的动态.
主要成果:
- 在没有明确的建模的情况下,D3IMC方案有效地控制未知的非线性非系.
- 标称控制算法确保对反错误的快速响应.
- 不确定性补偿算法有效地解决了模型植物不匹配和外部干扰的问题.
- 适应性机制提供了对系统不确定性的固有稳定性.
结论:
- 拟议的D3IMC方法与传统基于模型的IMC相比,是显著的进步.
- 这些数据驱动的方法为控制复杂系统提供了可行的替代方案,在这些复杂系统中,显式建模是具有挑战性的.
- 模拟研究验证了开发的D3IMC方案的有效性和稳定性.
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