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5. Matrix Models, Supervectors and Discrete Systems

6. Norm Optimal Iterative Learning Control

7. Combined Inverse and Gradient Based Design

8. Control Using Inverse Model Algorithms

9. Iterative Learning Control: A Formulation

10. Monotonicity and Gradient Algorithms

11. State Space Models

12. Mathematical Methods

13. Parameter Optimal Iterative Control

14. Acceleration and Successive Projection

15. Introduction

16. Iteration and Auxiliary Optimization

17. Iteration as Successive Projection

18. NOILC: Natural Extensions

21. Feedback and Optimal Control

27. Robustness

28. Conclusions and Further Work

29. Examples and Representations

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