Dynamic Encoding-Decoding-Based Quantized Iterative Learning Control

The tracking performance of linear discrete-time systems under quantized iterative learning control is investigated in this paper. An encoding-decoding mechanism is utilized to process the output of the system that is transmitted over the network channel. The encoding method includes the quantization procedure, which employs a uniform quantizer with finite quantization levels. The scaling parameters in the encoder and decoder are formulated to dynamically adjust based on the input of the quantizer. The learning control scheme is designed based on the output generated by the decoder. The results demonstrate that the system attains error-free tracking performance. The theoretical findings of this research are confirmed by a numerical simulation.