On General Linear Encoding-Decoding Pairs for Quantized Iterative Learning Control.

Area of Science:

• Control Systems Engineering
• Networked Control Systems
• Signal Processing

Background:

• Iterative Learning Control (ILC) faces challenges with limited bandwidth and quantization in networked systems.
• Existing encoding-decoding schemes for quantized ILC lack a unified framework.
• Data rate and quantization constraints significantly impact ILC performance.

Purpose of the Study:

• To design a general framework for linear encoding-decoding pairs in quantized ILC under channel constraints.
• To develop systematic guidelines for parameter selection in these pairs.
• To minimize tracking errors and ensure stability in quantized ILC systems.

Main Methods:

• Developed a unified mathematical framework integrating existing encoding-decoding schemes.
• Derived a convergence criterion for quantized ILC using a general linear encoding-decoding pair and a p-type controller.
• Introduced a Control Signal Fidelity Metric (CSFM) to quantify signal discrepancy.
• Established practical selection rules for finite-level quantizers.

Main Results:

• A unified framework for linear encoding-decoding pairs in quantized ILC was established.
• Convergence criteria and systematic guidelines for parameter selection were derived.
• Practical rules for finite-level quantizers minimize steady-state error and CSFM without saturation.
• Simulations on industrial robot joint models validated the theoretical findings.

Conclusions:

• The proposed framework effectively addresses quantization and channel constraints in ILC.
• The developed guidelines facilitate practical selection of encoding-decoding parameters for improved control performance.
• The study enhances the applicability of ILC in resource-constrained networked environments.