Zero-Error Tracking Control Under Unified Quantized Iterative Learning Framework via Encoding-Decoding Method.

Area of Science:

• Control Engineering
• Information Theory
• Networked Systems

Background:

• Iterative learning control (ILC) is crucial for repetitive tasks.
• Networked systems introduce challenges like data quantization and limited bandwidth.
• Achieving zero-error tracking in such systems is a significant problem.

Purpose of the Study:

• To investigate the zero-error tracking problem in quantized ILC for general networked systems.
• To develop and analyze an encoding-decoding mechanism for uniform quantizers in ILC.
• To prove the theoretical performance bounds and provide practical implementation guidelines.

Main Methods:

• Introduction of an encoding and decoding mechanism integrated with uniform quantizers.
• Quantization and transmission of system output data to the controller.
• Decoding, input generation for the next iteration, and subsequent transmission of control inputs.
• Mathematical proof of asymptotic zero-error tracking performance for infinite- and finite-level quantizers.

Main Results:

• Strict proof of asymptotic zero-error tracking performance for both infinite- and finite-level uniform quantizers.
• Development of a practical framework for quantized ILC in networked systems.
• Explicit selection of scaling sequences and quantization levels for finite-level quantizers.

Conclusions:

• The proposed quantized ILC scheme guarantees asymptotic zero-error tracking in networked systems.
• The encoding-decoding mechanism effectively handles data quantization and limited communication.
• The study provides a robust theoretical foundation and practical guidance for implementing quantized ILC.