On General Linear Encoding–Decoding Pairs for Quantized Iterative Learning Control

doi:10.1109/TCYB.2025.3633720

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On General Linear Encoding–Decoding Pairs for Quantized Iterative Learning Control

2025-11-25

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Taojun Liu

Dong Shen

Daniel W. C. Ho

DOI：10.1109/TCYB.2025.3633720

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Abstract

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As control systems increasingly rely on limited-bandwidth networks, quantization and data rate constraints present significant challenges for iterative learning control (ILC). This study aims to design a general framework of linear encoding–decoding pairs for quantized ILC under channel transmission constraints. We first develop a unified mathematical framework that integrates existing encoding–decoding schemes within the quantized ILC loop, enabling both the linear encoder and decoder designs to be parameterized by a common set. By employing a p-type controller, we derive a convergence criterion for quantized ILC using the general linear encoding–decoding pair. Furthermore, we introduce a control signal fidelity metric (CSFM) to quantify the discrepancy between the control signal generated with and without a general linear encoding–decoding pair. Based on the CSFM, we provide systematic guidelines for selecting the parameters of the linear encoding–decoding pair. Finally, we establish practical selection rules for the parameters of linear encoding–decoding pairs when finite-level quantizers are used. These rules ensure that no saturation occurs while minimizing both the steady-state output tracking error and the CSFM, thus facilitating the practical quantizer selection in quantized ILC. The theoretical findings are validated through simulations involving industrial robot joint models.

Keywords:

Channel transmission constraints

control signal fidelity metric (CSFM)

iterative learning control (ILC)

linear encoding–decoding pair

quantization