Performance Quantification of a Nonlinear Model Predictive Controller by Parallel Monte Carlo Simulations of a Closed-loop System

TL;DR

This paper introduces a parallel Monte Carlo simulation method to quantify the performance of nonlinear model predictive control (NMPC) in stochastic systems, demonstrating significant speed-up and performance advantages over PI controllers.

Contribution

The paper develops a high-performance, thread-safe NMPC implementation combined with Monte Carlo simulations to quantify closed-loop control performance efficiently.

Findings

Nearly linear scale-up on 32-core CPU

Approximately 27 times speed-up with 32 cores

NMPC outperforms PI controller in mean and variance

Abstract

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.