Encoding-Decoding Mechanism-Based Model-Free Adaptive Iterative Learning Control with Data Quantization and Data Dropouts

This paper focuses on tracking control for a specific class of discrete-time single-input single-output (SISO) nonlinear repetitive systems that encounter data quantization and dropouts during transmission. The output of the system requires quantization before transmission to the controller, and dropouts may occur. To address this issue, we introduce the encoding -decoding model-free adaptive iterative learning control (E-DMFAILC) algorithm. This algorithm leverages an encoding-decoding mechanism and intermittent update principle to manage data quantization and dropouts, reducing the communication load while preserving accurate tracking performance. Our research results show that the proposed algorithm achieves zero convergence of tracking error by using only a small amount of input and output (I/O) data. Finally, we verify the effectiveness of the algorithm through simulation experiments, which further confirm the superiority of E-DMFAILC.