测量数据部分丢失的非线性系统迭代学习控制

研究非线性离散系统的迭代学习控制．利用并行分配补偿方法(PDC)确定非线性系统的T-S模型,把非线性模型特换为局部线性模型。假定数据丢失的概率已知,采用满足Bernoulli分布的序列来描述测量数据的丢失,研究具有测量数据部分丢失的线性离散系统的迭代学习控制器的设计。结合T-S模型设计了数据丢失的迭代学习控制器,所设计的迭代学习控制器具有期望收敛特性和二次型性能指标。仿真结果表明了该设计方法的有效性。

Iterative Learning Control for Nonlinear Systems with Missing Data

Based on the Takagi-Sugeno fuzzy model,a fuzzy iterative learning control scheme is developed by the parallel distributed compensation(PDC)method.For packet-based transmission of data over a network,or temporary sensor failure,etc.,data samples may be missed in measured signals.The missed measurements happen at any sample time,and the probability of the occurrence of missing data is assumed to be known.The series meeting the Bernoulli distribution is used to describe missed measure- ments.The fuzzy iterative learning controller guarantees the expected convergence of the tracking error and has quadratic performance indices.Simulation example demonstrates the validity of the design ap- proach.