Saturated Finite Interval Iterative Learning for Tracking of Dynamic Systems With HNN-Structural Output

This brief investigates the interval iterative learning problem for dynamic systems with hierarchical neural network (HNN)-structural output. The first objective is to design the output of a dynamic system with HNN structure. A sufficient condition is obtained to achieve the interval tracking in a finite interval by applying iterative learning control (ILC). Then, the saturated ILC is considered into the discussed system, and a less conservative criterion is obtained to achieve the tracking in a finite interval using a network structure decomposition technique. Finally, simulation results are given to illustrate the usefulness of the developed criteria.