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Data-driven control for relative degree systems via iterative learning.

Deyuan Meng¹, Yingmin Jia, Junping Du

¹Department of Systems and Control and the Seventh Research Division, Beihang University, Beijing 100191, China. dymeng23@126.com

IEEE Transactions on Neural Networks

|November 25, 2011

Summary

This summary is machine-generated.

This study introduces a unified 2-D analysis for iterative learning control (ILC) systems. The approach ensures asymptotic stability and monotonic convergence for both continuous and discrete-time ILC, enhancing performance and robustness.

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Area of Science:

Control Systems Engineering
Data-Driven Control
Systems Theory

Background:

Iterative learning control (ILC) is a data-driven technique using input/output data.
Existing ILC analysis often struggles with systems of varying relative degrees.
A unified framework is needed for robust ILC system analysis.

Purpose of the Study:

To develop a unified 2-D analysis framework for continuous-time and discrete-time ILC systems.
To establish convergence conditions guaranteeing asymptotic stability and monotonic convergence.
To provide a method for deriving ILC updating laws and analyzing robustness.

Main Methods:

Utilizing the 2-D Roesser system framework for general ILC systems.
Deriving convergence conditions based on the 2-D system representation.
Employing linear matrix inequalities (LMIs) to specify monotonic convergence conditions.

Main Results:

A unified 2-D analysis framework is established for ILC systems, irrespective of relative degree.
Convergence conditions ensuring asymptotic stability and monotonic convergence are provided.
The method effectively addresses higher-order relative degree problems and demonstrates robustness.

Conclusions:

The proposed 2-D analysis approach offers a unified and effective method for ILC system design and analysis.
The framework guarantees essential performance criteria like stability and monotonic convergence.
This approach enhances the applicability of ILC to complex systems with uncertainties.