Tracking Under Saturated Finite Interval and HNN-Structural Output

ILC has received attention up until this point due to its many industrial uses, including robotic manipulators, hard disk drives, quick thermal processing, and chemical polymerization/crystallization, see Kurek and Zaremba (1993), Wang et al. (2004), Liu and Jia (2012), Meng et al. (2012), Li and Li (2012). The goal of ILC designs is to use repetition to enhance tracking performance in a finite-time interval despite having incomplete knowledge of the dynamics structure and parameter values. A direct adaptive iterative learning control is provided in Wang et al. (2004) for a class of repeated nonlinear systems with unknown nonlinearities and variable initial resetting errors. This control is based on a new output-recurrent fuzzy neural network. The formation problem is equivalently transformed into a stability control problem over finite-time intervals by using the iterative learning approach, as demonstrated in Liu and Jia (2012). According to in Li and Li (2012), all follower agents maintain the necessary distance from the leader and reach velocity consensus uniformly on the finite interval [0, T] for the formation problem, whereas all follower agents track the leader uniformly on the finite interval [0, T] for the consensus problem.