Autonomous robots deployed in unknown search-and-rescue (SaR) environments can significantly improve the efficiency of the mission by assisting in fast localisation and rescue of the trapped victims. We propose a novel integrated hierarchical control architecture, called model predictive fuzzy control (MPFC), for autonomous mission planning of multi-robot SaR systems that should efficiently map an unknown environment: We combine model predictive control (MPC) and fuzzy logic control (FLC), where the robots are locally controlled by computationally efficient FLC controllers, and the parameters of these local controllers are tuned via a centralised MPC controller, in a regular or event-triggered manner. The proposed architecture provides three main advantages: (1) The control decisions are made by the FLC controllers, thus the real-time computation time is affordable. (2) The centralised MPC controller optimises the performance criteria with a global and predictive vision of the system dynamics, and updates the parameters of the FLC controllers accordingly. (3) FLC controllers are heuristic by nature and thus do not take into account optimality in their decisions, while the tuned parameters via the MPC controller can indirectly incorporate some level of optimality in local decisions of the robots. A simulation environment for victim detection in a disaster environment was designed in MATLAB using discrete, 2-D grid-based models. While being comparable from the point of computational efficiency, the integrated MPFC architecture improves the performance of the multi-robot SaR system compared to decentralised FLC controllers. Moreover, the performance of MPFC is comparable to the performance of centralised MPC for path planning of SaR robots, whereas MPFC requires significantly less computational resources, since the number of the optimisation variables in the control problem are reduced.