An alternative robust model predictive control (MPC) design method is proposed. This approach is based on the use of a generalized objective function. It exploits the similarity between the parameter estimation problem and MPC. Robustness is achieved by choosing the objective function according to the error probability density function, as in robust system identification. The stability of the controller is demonstrated through the nominal asymptotic stability and that the plant cost can be located in the demanded vicinity of the non-increasing nominal cost, given a suitable objective function. One advantage of the proposed method is its relative simplicity regarding to the problem of choosing suitable model uncertainty descriptions in other robust approaches. The performance of the proposed method is illustrated by a chemical engineering example. (C) 2003 Elsevier Science Ltd. All rights reserved.