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Adaptive Iterative Learning Reliable Control of Nonrepetitive Systems With Multiple Iteration-Varying Parametric
IEEE Transactions on Cybernetics
|February 13, 2026
Summary
This study introduces an adaptive iterative learning control scheme for nonrepetitive systems, overcoming previous limitations. The new method effectively handles uncertainties, actuator faults, and state delays for improved practical applications.
Area of Science:
- Control Engineering
- Adaptive Control Systems
- Nonlinear Systems
Background:
- Iterative learning control (ILC) is hindered by its repetitiveness requirement.
- Practical applications of ILC are limited by nonrepetitive systems and uncertainties.
- Existing control methods struggle with simultaneous actuator faults and state delays.
Purpose of the Study:
- To propose a novel adaptive iterative learning reliable control (ILRC) scheme.
- To address nonrepetitive systems with iteration-varying parametric uncertainties.
- To simultaneously consider actuator faults and state delays in the ILRC design.
Main Methods:
- Leveraging class- $k_{\infty }$ functions and neural networks to manage unmodeled dynamics.
- Implementing control signal transformation to compensate for actuator inefficiencies.
- Developing an innovative parametric estimation mechanism with hyperbolic tangent functions and auxiliary sequences for nonrepetitive uncertainties.
Main Results:
- Achieved zero-error convergence of system output.
- Successfully compensated for actuator faults and state delays.
- Demonstrated effective handling of iteration-varying parametric uncertainties.
Conclusions:
- The proposed ILRC scheme offers enhanced performance and practicality over existing methods.
- The approach requires weak assumptions on system dynamics and minimal prior knowledge of uncertainties.
- The controller exhibits strong learning capabilities, making it suitable for complex, nonrepetitive systems.
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