Data-driven model-free adaptive iterative learning control for a class of discrete-time nonlinear systems
离散时间非线性系统的数据驱动无模型自适应迭代学习控制

In this paper, a data-driven model-free adaptive iterative learning control (MFAILC) scheme is proposed based on a novel dynamic linearization approach along the iteration axis for a class of repetitive discrete-time single input single output (SISO) nonlinear systems. The MFAILC is essentially a data-driven control method that designs controller merely using the measured input and output data of the controlled plant. Theoretical analysis shows that the MFAILC guarantees the monotonic convergence of the iteration maximum error. Numerical example and freeway traffic control application are given to illustrate the effectiveness of the MFAILC.