  The Renyi entropy is an important measure of the information, it is proposed
by Renyi in the context of entropy theory as a generalization of Shannon
entropy. We study in detail this measure of multivariate autoregressive moving
average (ARMA) control systems. The characteristic function of output process
is represented from the terms of its residual characteristic functions. Simple
expression to compute the Renyi entropy for the output process of these systems
is derived. In addition, we investigate the covariance matrix for finding the
upper bound of the entropy. Finally, we present three separate examples that
serve to illustrate the behavior of information in a multivariate ARMA control
system where the control and noise distributed as Gaussian, Cauchy and Laplace
processes.
