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Model predictive control (MPC) has developed considerably in the last decades both in industry and in academia. This success is due to the fact that MPC is perhaps the most general way of posing the control problem in the time domain. One of the main advantages of MPC is that model uncertainties can be taken explicitly into account and this allows for the consideration of faulty process behavior. The receding control strategy used in MPC can be extended to the case of system identification by parameter bounding and furthermore to determine if a model is consistent with the obtained data in a receding horizon manner and this implicitly allows for fault detection. The paper shows how concepts arising from the fault detection and fault-tolerant design methods can be incorporated in an MPC framework and the advantages that can be gained by using MPC in this context.