Distributed Predictive Consensus Control of Uncertain Linear Multi-agent Systems with Heterogeneous Dynamics

This paper investigates the consensus of distributed model predictive control for n-dimensional linear multi-agent systems with uncertain and heterogeneous dynamics, with the same dimensions. Output tracking and regulation of the multi-agent uncertain systems have been studied in recent years, but there are a few efficient studies regarding polytopic and multi-model uncertainties in multi-agent systems. The problem is constrained, and the graph of the system is directed, without any leaders. In addition, two types of model uncertainties are considered; polytopic and multi-model uncertainties. First, the consensus is achieved using linear matrix inequalities and slack variables and proper conditions for the consensus of the system are provided via linear matrix inequalities, and then the same result is achieved with a closed-loop prediction strategy. Finally, simulation of the consensus of linear multi-agent systems in the presence of polytopic and multi-model uncertainties is conducted to evaluate the efficiency of the approach.