The goal of this paper is to investigate the theoretical properties, the
training algorithm, and the predictive control applications of Echo State
Networks (ESNs), a particular kind of Recurrent Neural Networks. First, a
condition guaranteeing incremetal global asymptotic stability is devised. Then,
a modified training algorithm allowing for dimensionality reduction of ESNs is
presented. Eventually, a model predictive controller is designed to solve the
tracking problem, relying on ESNs as the model of the system. Numerical results
concerning the predictive control of a nonlinear process for pH neutralization
confirm the effectiveness of the proposed algorithms for the identification,
dimensionality reduction, and the control design for ESNs.Comment: 6 pages,5 figures, submitted to European Control Conference (ECC