  We present an approach for accelerating nonlinear model predictive control.
If the current optimal input signal is saturated, also the optimal signals in
subsequent time steps often are. We propose to use the open-loop optimal input
signals whenever the first and some subsequent input signals are saturated. We
only solve the next optimal control problem, when a non-saturated signal is
encountered, or the end of the horizon is reached. In this way, we can save a
significant number of NLPs to be solved while on the other hand keep the
performance loss small. Furthermore, the NMPC is reactivated in time when it
comes to controlling the system safely to its reference.
