Fuzzy iterative learning control for nonlinear systems with missing data

For packet-based transmission of data over a network, or temporary sensor failure, etc:, data samples may be missing in the measured signals. The missing measurements will happen at any sample time, and the probability of the occurrence of missing data was assumed to be known. The series which fulfils Bernoulli distribution was used to describe the missing measurements. Based on the Takagi-Sugeno fuzzy model, nonlinear system was represent by T-S fuzzy, model via the so-called parallel distributed compensation(PDC) approach. The fuzzy iterative learning controller was developed to guarantee the expected convergence of the tracking error and with quadratic performance index. A numerical example was provided to demonstrate the validity of the proposed design approach.