Optimal Iterative Learning Control of Quantized Signals Based on Encoding-Decoding Method

This paper considers optimal iterative learning control (ILC) to solve the trajectory tracking problem of systems using a network to transmit signals. Assuming that the relative quantization error of the logarithmic quantizer is white noise uniformly distributed in a known range, a cost function in the sense of mathematical expectation is established to deal with the relative quantization error, so as to obtain an iterative update law. By combining the logarithmic quantizer with the encoding and decoding method, the system can iteratively achieve high-precision tracking with limited communication capacity. Simulations based on an electro-mechanical control system model is provided to validate the proposed method.