Current approaches to the cooperative control of network systems are based on a priori knowledge about the (follower) system dynamics: Either the dynamics are known, or assumed to be minimum phase, or initial stabilizing controllers are available for each system. The purpose of this article is to show that for single-input single-output systems (SISO) the above assumptions can be relaxed. We propose an indirect adaptive methodology that does not require the knowledge of the parameters of the systems, or the systems to be minimum phase, or initial stabilizing controllers, in order to guarantee asymptotic tracking.

We analyze adaptive synchronization capabilities in networks with Kuramoto-like units whose dynamical features are unknown and thus synchronization protocols must exhibit co-evolution capabilities. In the presence of heterogeneous and uncertain units, synchronization should be enabled by appropriate adaptive protocols that counteract the effect of heterogeneity. An interaction protocol is presented that is used by the units to communicate with each other: the protocol is based on a distributed disagreement measure. The aim of the protocol is to adapt feedback and coupling gains, so as to guarantee the emergence of a synchronous solution. The adaptive strategy is distributed, i.e. each unit self-determines the strength of its gains by using only neighboring measurements. Convergence of the synchronization error to zero is shown via Lyapunov analysis, and numerical examples demonstrate the effectiveness of the proposed protocol.

In networks with heterogeneous and uncertain agents, fixed-gain control can lead to synchronization only if the uncertainties are relatively small. If the uncertainties are larger, we need to develop adaptive-gain approaches to achieve synchronization. In this work we propose an adaptive synchronization protocol, in case of full-state measurement, for uncertain heterogeneous agents based on a distributed disagreement reasoning. Specifically, we first define unknown gains (feedback and coupling gains) that could lead all agents to a homogeneous behavior and thus synchronization: however, since these gains are unknown in view of the unknown dynamics, we design adaptive laws for these gains that lead the agents toward synchronization. The adaptive laws are driven by a disagreement error which is calculated among neighbors: a Lyapunov analysis is presented for showing convergence of the synchronization error to zero.