Distributed Algorithm for Higher-Order Integrators to Track Average of Unbounded Signals. Issue 2 (2020) - Sen, Arijit; Sahoo, Soumya Ranjan; Kothari, Mangal
Abstract: In this work, we design an algorithm for a group of higher-order integrators aiming to track the average of multiple time-varying and possibly unbounded reference signals. The existing literature has studied distributed average tracking (DAT) for higher-order systems in the presence of bounded or Lipschitz-type reference signals. In such DAT algorithms, each agent requires the knowledge of global bounds on signals for bounded references and state-dependent control gains for unbounded references. Addressing these issues, we propose a DAT algorithm for a group of higher-order integrators in the presence of time-varying references that can possibly be unbounded. The highest derivative of references become equal, asymptotically. Agents use neighbors' data obtained from the local communication framework that makes the current algorithm distributed in nature. In contrast to existing work, our DAT algorithm uses constant gains to reduce high control effort, which may be caused due to state-dependent gains. Using numerical example, the performance of the current algorithm is compared with the existing state-of-the-art. This reveals the superiority of the proposed algorithm.