Distributed Iterative Learning Control for Networked Dynamical Systems with Guaranteed Individual Energy Cost

High performance formation control problem working repetitively has found important applications in various areas. Recent design uses iterative learning control (ILC) to achieve the high performance requirements, since ILC does not require a highly accurate model required by traditional control methods. This paper considers a previously unexplored problem in formation control problem, which aims at achieving the high performance requirement while guaranteeing individual input energy cost using only local information. We propose two novel ILC algorithms to solve the above problem. The proposed algorithms are suitable for both homogeneous and heterogeneous networks, as well as non-minimum phase systems, which are appealing in practice. Distributed implementations using the alternating direction method of multiplies are provided, allowing the proposed algorithms to be applied to large scale networked dynamical systems. Convergence properties of the algorithms are analysed rigorously and numerical examples are presented to demonstrate their effectiveness.