标题： 基于闭环系统的并行蒙特卡洛仿真量化非线性模型预测控制器的性能 显示英文标题

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

摘要： 本文提出了一种基于并行蒙特卡洛模拟的非线性模型预测控制（NMPC）闭环性能量化方法。该方法为随机系统的控制器性能提供了分布，从而实现性能量化。 我们利用一种新的线程安全的NMPC实现与现有的高性能C语言蒙特卡洛模拟工具箱相结合，执行了高性能的蒙特卡洛模拟。我们将NMPC调节器表达为一个最优控制问题（OCP），并通过新的线程安全顺序二次规划软件NLPSQP求解。 我们的结果显示，在32核CPU上的NMPC闭环几乎呈线性扩展。特别是，我们在32核上获得了大约27倍的速度提升。 我们在一个简单的连续搅拌反应器（CSTR）上展示了这种性能量化方法，进行了30,000次NMPC和参考比例积分（PI）控制器的闭环模拟。 随机闭环系统的性能量化显示，NMPC在均值和方差方面都优于PI控制器。 显示英文摘要

摘要： 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.