A Distributed Indirect Adaptive Approach to Cooperative Tracking in Networks of Uncertain Single-Input Single-Output Systems

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.

Automated summary

This article presents an indirect adaptive methodology for the cooperative control of single-input single-output systems, which does not require prior knowledge of system parameters, minimum phase assumptions, or initial stabilizing controllers to guarantee asymptotic tracking.