Iterative Learning Control of Constrained Systems With Varying Trial Lengths Under Alignment Condition.

Area of Science:

• Control Engineering
• Nonlinear Systems Theory
• Adaptive Control

Background:

• Iterative learning control (ILC) is crucial for repetitive tasks in nonlinear systems.
• Constrained multi-input multi-output (MIMO) systems present challenges due to complexity and varying operational conditions.
• Achieving state alignment and bounded convergence is essential for reliable control performance.

Purpose of the Study:

• To develop an adaptive ILC scheme for constrained MIMO nonlinear systems.
• To address the state alignment condition with varying trial lengths.
• To ensure bounded convergence of the closed-loop system using a novel approach.

Main Methods:

• A modified reference trajectory is constructed to ensure spatial closure and meet the state alignment condition.
• The barrier composite energy function (BCEF) approach is employed for stability analysis.
• An adaptive ILC algorithm is designed to handle system uncertainties and constraints.

Main Results:

• The proposed adaptive ILC scheme guarantees bounded convergence of the closed-loop system under the state alignment condition.
• The method effectively handles systems with varying trial lengths.
• Illustrative examples confirm the validity and performance of the developed iterative scheme.

Conclusions:

• The presented adaptive ILC strategy offers a robust solution for controlling constrained MIMO nonlinear systems.
• The BCEF approach provides a rigorous framework for analyzing stability and convergence.
• This work advances the application of ILC in complex, real-world control scenarios.