  This paper presents a parallel Monte Carlo simulation based performance
quantification method for nonlinear model predictive control (NMPC) in
closed-loop. The method provides distributions for the controller performance
in stochastic systems enabling performance quantification. We perform
high-performance Monte Carlo simulations in C enabled by a new thread-safe NMPC
implementation in combination with an existing high-performance Monte Carlo
simulation toolbox in C. We express the NMPC regulator as an optimal control
problem (OCP), which we solve with the new thread-safe sequential quadratic
programming software NLPSQP. Our results show almost linear scale-up for the
NMPC closed-loop on a 32 core CPU. In particular, we get approximately 27 times
speed-up on 32 cores. We demonstrate the performance quantification method on a
simple continuous stirred tank reactor (CSTR), where we perform 30,000
closed-loop simulations with both an NMPC and a reference proportional-integral
(PI) controller. Performance quantification of the stochastic closed-loop
system shows that the NMPC outperforms the PI controller in both mean and
variance.
