MATLAB Implementation of Iterative Learning Control with Code and Simulink Integration

This MATLAB implementation of iterative learning control combines M-file scripts with Simulink models to create a comprehensive control system solution. Iterative learning control (ILC) is a model-reference adaptive control methodology designed for online parameter adjustment and system performance optimization. The program implements ILC algorithms through MATLAB code that handles the core control logic, including error computation, learning law application, and control signal generation. Key functions likely involve iterative update mechanisms using previous cycle data to improve current cycle performance. The Simulink component provides a graphical modeling environment for system representation, enabling seamless integration between the control algorithm and plant dynamics. This implementation demonstrates how to connect MATLAB algorithms with Simulink models through S-functions or MATLAB function blocks, allowing real-time parameter tuning and performance monitoring. The combination enables users to simulate system behavior under ILC strategies, analyze convergence properties, and validate control effectiveness through graphical simulations. Through this program, engineers can gain practical understanding of ILC principles, including learning filter design, stability analysis, and implementation techniques for adaptive control systems. The code structure showcases proper handling of iterative data storage, cycle synchronization, and performance evaluation metrics typical in ILC applications.