Iterative Rectifying Methods for Nonrepetitive Continuous-Time Learning Control Systems.

Area of Science:

• Control Systems Engineering
• Automation and Robotics

Background:

• Iterative Learning Control (ILC) relies on system repetitiveness, limiting its application to systems with nonrepetitive uncertainties.
• Continuous-time systems with iteration-dependent uncertainties pose challenges for traditional ILC methods.

Purpose of the Study:

• To develop a robust iterative learning control (ILC) strategy for continuous-time systems.
• To address tracking problems in the presence of nonrepetitive uncertainties, including system models, initial states, disturbances, and references.

Main Methods:

• Integration of an iterative rectifying mechanism to mitigate nonrepetitive uncertainties.
• Development of a robust convergence analysis combining contraction mapping and system equivalence transformation methods.

Main Results:

• The proposed ILC method effectively handles multiple nonrepetitive uncertainties in continuous-time systems.
• Robust tracking is achieved for systems with zero or nonzero relative degrees, ensuring trajectory boundedness.
• Validation through two illustrative examples confirms the effectiveness of the robust tracking results.

Conclusions:

• The developed robust ILC approach enhances the applicability of ILC to real-world continuous-time systems with uncertainties.
• The method provides a theoretical framework and practical solution for robust tracking in nonrepetitive ILC scenarios.