The paper presents a model predictive control (MPC) algorithm for continuous-time, possibly non-square nonlinear systems. The algorithm guarantees the tracking of asymptotically constant reference signals by means of a control scheme were the integral action is directly imposed on the error variables rather than on the control moves. The plant under control, the state and control constraints and the performance index to be minimized are described in continuous time, while the manipulated variables are allowed to change at fixed and uniformly distributed sampling times. The algorithm is used to control a continuous fermenter where the manipulated variables are the dilution rate and the feed substrate concentration while the controlled variable is the biomass concentration. (c) 2007 Elsevier Ltd. All rights reserved.