Title: Model Predictive Control Tuning by Monte Carlo Simulation and Controller Matching Show Chinese title

Title: 基于蒙特卡洛仿真和控制器匹配的模型预测控制参数调节

Abstract: This paper presents a systematic method for the selection of the Model Predictive Control (MPC) stage cost. We match the MPC feedback law to a proportional-integral (PI) controller, which we efficiently tune by high-performance Monte Carlo (MC) simulation. The PI tuning offers a wide range of tuning possibilities that is then inherited by the MPC design. The MC simulation tuning of the PI controller is based on the minimization of two different objectives; 1) the 2-norm tracking error, and 2) a bi-objective consisting of the 2-norm tracking error and a 2-norm input rate of movement penalty. We apply the method to design MPC for an exothermic chemical reaction conducted in an adiabatic continuous stirred tank reactor (CSTR). The process is of interest as the nonlinear dynamics result in a desired operating point very close to a constraint. Our MPC design includes stage costs automatically designed to match the tuned PI controllers, hard input constraints, and a soft output constraint. Stochastic simulation results show that both the PI controller and the MPC can track the desired operating point. However, the MPC shows reduced output constraint violation compared to the PI controller. As such, the MPC design method successfully combines the efficient tuning of the PI controller with the constraint handling properties of MPC. Show Chinese abstract

Abstract: 本文提出了一种系统的方法来选择模型预测控制（MPC）阶段代价函数。我们将MPC反馈律与比例积分（PI）控制器相匹配，并通过高性能蒙特卡罗（MC）仿真高效地调整PI控制器。这种PI控制器的调节提供了广泛的调节可能性，然后这些可能性被继承到MPC设计中。基于最小化两种不同目标的PI控制器的MC仿真调节包括：1）2-范数跟踪误差；2）由2-范数跟踪误差和2-范数输入变化惩罚组成的双目标。我们将该方法应用于设计一个绝热连续搅拌釜式反应器（CSTR）中的放热化学反应的MPC。该过程因其非线性动态特性导致期望操作点非常接近约束条件而具有研究价值。我们的MPC设计自动包含了与调谐后的PI控制器匹配的阶段代价函数、硬输入约束以及软输出约束。随机仿真结果显示，PI控制器和MPC都能够跟踪期望的操作点。然而，与PI控制器相比，MPC显示出减少的输出约束违反。因此，MPC设计方法成功结合了PI控制器的高效调节与MPC的约束处理特性。