Distributed Norm Optimal Iterative Learning Control for Point-to-Point Consensus Tracking

High performance consensus tracking of networked dynamical systems working repetitively is an important class of coordination problems and it has found many applications in different areas. Recently, iterative learning control (ILC), which does not require a highly accurate model to achieve the high performance requirement, has been developed for the consensus tracking problem. Most of existing ILC algorithms consider about the tracking of a reference defined over the whole trial length, while the Point-to-Point (P2P) task where the emphasis is placed on the tracking of intermediate time instant points, has not been explored. To bridge this gap, we develop a norm optimal ILC (NOILC) algorithm for P2P consensus tracking problem that guarantees not only the monotonic convergence of consensus tracking error norm to zero, but also the convergence of input to the minimum input energy solution, which is desired in practice. Moreover, using the idea of the alternating direction method of multipliers, we develop a distributed implementation method for the proposed algorithm, allowing the resulting algorithm to be applied to large scale networked dynamical systems. Rigorous analysis of the algorithm's properties is provided and numerical simulations are given to verify its effectiveness. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.