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The use of fuzzy goals and fuzzy constraints in predictive control allows for a more flexible aggregation of the control objectives than the usual weighted sum of squared errors. A multistage decision making algorithm is applied to compute the optimal control action. Compared to the standard quadratic objective function, with the fuzzy decision making approach, the designer has more freedom in specibing the desired process behaviol: This paper presents an experimental comparison of different cost functions, using an example of container crane control. The conventional quadratic criterion is compared with a conjunctive aggregation of fuzzy goals. The results show that a better performance can be achieved by using fuzzy goals. On the other hand, the optimization problem associated with the multistage decision making procedure has higher computational demands.Keywords: Fuzzy predictive control, fuzzy decision making, fuzzy goals and constraints, optimization, predictive control.Complex, nonlinear and partially known systems, encountered for instance in the chemical process industry, biotechnological processes or climate control, present big challenges for automatic control. While the conventional linear control techniques sometimes fail or can be applied only locally, human operators are able to control these systems across a wide range of operating conditions. Knowledgebased control tries to integrate the knowledge of human operators or process engineers into the controller design. Fuzzy control, one of the most popular techniques, has been