模型未知非线性时变系统的增量学习与状态空间演化模糊控制
Incremental Learning and State-Space Evolving Fuzzy Control of Nonlinear Time-Varying Systems with Unknown Model
摘要：
提出了一种对未知非线性系统进行增量式建模和时变控制的方法。该方法结合了进化智能，颗粒机器学习和多变量控制的元素。提出了一种基于状态空间模糊集的演化建模方法。由此产生的模糊模型的结构和参数开发的数据流，重点是内存和数据覆盖率。模糊控制器也进化，基于数据实例和模糊模型参数。它的局部增益被重新设计的实时-每当相应的局部模糊模型的变化-从一个线性矩阵不等式问题的解决方案，从一个模糊李雅普诺夫函数和有界输入条件。我们证明了Henon混沌的一步预测和渐近稳定性。
英文摘要：
We present a method for incremental modeling and time-varying control of unknown nonlinear systems. The method combines elements of evolving intelligence, granular machine learning, and multi-variable control. We propose a State-Space Fuzzy-set-Based evolving Modeling (SS-FBeM) approach. The resulting fuzzy model is structurally and parametrically developed from a data stream with focus on memory and data coverage. The fuzzy controller also evolves, based on the data instances and fuzzy model parameters. Its local gains are redesigned in real-time -- whenever the corresponding local fuzzy models change -- from the solution of a linear matrix inequality problem derived from a fuzzy Lyapunov function and bounded input conditions. We have shown one-step prediction and asymptotic stabilization of the Henon chaos.
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