Iterative Learning Control for Batch-varying References

This paper presents iterative learning control (ILC) schemes for batch-varying references. Generally, reference or target trajectory must be identical for all iterations to implement the ILC. However, references can be changed in dynamic systems such as robotics and chemical processes according to cycle or batch. ILC schemes for batch-varying references are proposed in three forms which are inverse of model-based ILC (I-ILC), quadratic-criterion-based ILC (QILC), and general norm optimal ILC form. These control schemes are studied for discrete linear time invariant (LTI) system. A numerical example is provided to demonstrate the performance of the proposed algorithms.