Iterative consensus control for a class of nonlinear MIMO multi-agent systems with data dropout

Abstract

Consider frequent data dropout phenomena of measurements due to communication channel failure. The consensus tracking problem for a class of nonlinear MIMO multi-agent systems (MAS) with data dropouts is addressed in this paper. The phenomena of data dropout is described by an introduced random variable, which is defined with a Bernoulli sequence valued 0/1. Moreover, the situation which only part of the agents can access the desired trajectory information also take into account. Based on the random variable, a modified distributed P-type iterative learning control algorithm with data drops is proposed. In addition, the identical initialization condition of classic iterative learning control (ILC) is released as well. Then, the theoretical analysis of the system convergence condition is given by using the contraction mapping method and supermom norm technical. As a result, the proposed ILC algorithm can guaranteed the system convergence with data dropout in a finite time. Finally, an illustrative example is presented to demonstrate the perfect tracking performance and the effectiveness of the scheme.