The identification of high fidelity models is a critical element in the implementation of high performance model predictive control (MPC) applications in the industry. These controllers can vary in size with input-ouput dimensions ranging from 5 Chi 10 to 50 Chi 100. Identifying models of this scale accurately is a time consuming and demanding exercise. We present a novel approach wherein an information rich test signal is generated in closed loop by maximizing the MPC objective, as opposed to minimization that is done in the standard controller. We show that the proposed input design approach is similar to T-optimal (trace optimal) experiment design method. Our approach automatically accounts for the input and output constraints and is implemented in a moving horizon manner. It is demonstrated through simulation examples on both well and ill-conditioned processes. (C) 2013 Elsevier Ltd. All rights reserved.