Set membership identification is a method for building a mathematical model of a dynamic system from experimental data. Based on the initially available information on the plant that is being identified, the knowledge of a bound on the disturbance signal that affects the obtained measurements and the measurements themselves, it provides a set of all possible plant models that are consistent with the available data. The resulting uncertainty model obtained when set membership identification is used is deterministic in contrast to the probability density function which represents the identification uncertainty when probabilistic identification is used. The assumptions made by set membership identification are often less restrictive than the assumptions made by probabilistic identification. Two topics related to set membership identification and its application to controller design will be discussed in the talk. The first topic is an adaptive model predictive control algorithm for constrained, multiple input, multiple output linear systems that is based on set membership identification. Use cases of practical and experimental application of this algorithm will be presented. The second topic is a novel on-line direct controller design method for nonlinear systems that uses set membership identification. The technique does not derive explicitly a model of the system, rather it delivers directly the feedback controller by combining an on-line and an off-line scheme.