  This paper presents a novel, safe control architecture (SCA) for controlling
an important class of systems: safety-critical systems. Ensuring the safety of
control decisions has always been a challenge in automatic control. The
proposed SCA aims to address this challenge by using a Model Predictive
Controller (MPC) that acts as a supervisor for the operating controller, in the
sense that the MPC constantly checks the safety of the control inputs generated
by the operating controller and intervenes if the control input is predicted to
lead to a hazardous situation in the foreseeable future invariably. Then an
appropriate backup scheme can be activated, e.g., a degraded control mechanism,
the transfer of the system to a safe state, or a warning signal issued to a
human supervisor. For a proof of concept, the proposed SCA is applied to an
autonomous driving scenario, where it is illustrated and compared in different
obstacle avoidance scenarios. A major challenge of the SCA lies in the mismatch
between the MPC prediction model and the real system, for which possible
remedies are explored.
