DISCRETE-TIME ITERATIVE LEARNING CONTROL FOR NONLINEAR SYSTEMS BASED ON FEEDBACK LINEARIZATION

Iterative learning control (ILC) learns to track a pre-defined maneuver with high accuracy through practice. It aims to approach the hardware reproducibility error level, which is usually beyond the accuracy of the system model used in the learning process. ILC can be used in spacecraft fine pointing sensors doing repeated scanning maneuvers. This paper considers use of feedback linearization in ILC, coupled with sophisticated linear ILC laws. Previous papers in this seried study other methods of extending ILC to nonlinear systems, using linearlization, and bilineariarization. Comparison is made of the three approaches. Feedback linearization has the advantage that it provides a global linear model to the ILC law while linearization and bilinearization are local models. Numerical examples demonstrate the comparison of these ILC methods for nonlinear systems, suggesting that feedback linearization based ILC can exhibit faster learning.