Zero-Error Tracking Control Under Unified Quantized Iterative Learning Framework via Encoding–Decoding Method
Dong Shen, Chao Zhang
Abstract
This article considers the zero-error tracking problem of quantized iterative learning control for a general networked structure where the data are quantized and transmitted through limited communication channels at both measurement and actuator sides. An encoding and decoding mechanism is introduced into a simple uniform quantizer. The system output is first encoded and quantized and then transmitted to the controller. When the data are received, they are decoded and applied to generate the input for the next iteration. After that, the generated input is transmitted following the same procedure as the output transmission, that is, encoding, quantizing, transmitting, and decoding. For this learning tracking framework, the asymptotic zero-error tracking performance is strictly proved for both infinite- and finite-level uniform quantizers. For practical implementation, a promising selection of the scaling sequences and the associated quantization level for the finite-level case is explicitly defined. Simulations are provided to demonstrate the effectiveness of the proposed schemes.