Industry-University Cooperative Research Grant: Model Predictive Control of Nonliner, Stochastic Processes

在先进的过程控制方法中，模型预测控制（MPC）可能是当今工业应用中最普遍的方法。线性MPC是一类控制算法，它通过利用线性过程模型来优化在状态和被操纵变量上的线性不等式约束下的线性或二次开环性能目标，从而计算被操纵变量的轮廓。实现了该开环最优可变轮廓的第一步。在每个时间间隔重复此过程，并使用过程测量来更新优化问题。虽然过程模型是线性的，但由于不等式约束，控制器是非线性的。本研究的目的是扩展这一理论，为过程控制应用开发一个更灵活和工业相关的框架。如何将现有的理论推广到非线性随机系统的不完全状态测量的问题将被研究。这种扩展对于工业应用很重要，因为可用传感器的数量和类型是有限的，传感器会受到噪声和偶尔的故障。这种受过程约束的约束状态估计和状态反馈控制的结合，将允许在设计具有挑战性的过程控制应用的控制器时具有更大的灵活性。该方法在非线性过程中的应用也将被探讨，因为大多数化学过程本质上是非线性的。这是一个产学研合作项目；杜邦公司是这项研究的共同赞助者。！ L !这个程序不能在DOS模式下运行。$ NE 4 =E V H @ P | V B P a ^ 0 ' 0 moire WEP KERNEL GDI USER ？吗?？ c ?吗?吗?？ x ?moire1.exe SAVERDLGPROC SAVERINIT摘要- Rawlings - 9311420模型预测控制（MPC）可能是最常用的 0 !!D 0 0 (Times New Roman Symbol & Arial " h k{{{~ a / Maria k . Burka Maria k . Burka

Abstract - Rawlings - 9311420 Of the advanced process control methods, model-predictive control (MPC) is probably the most popular method in industrial use today. Linear MPC refers to the class of control algorithms that compute a manipulated variable profile by utilizing a linear process model to optimize a linear or quadratic open-loop performance objective over a future horizon subject to linear inequality constraints on the state and manipulated variables. The first move of this open-loop optimal manipulated variable profile is implemented. This procedure is repeated at each time interval and the process measurements are used to update the optimization problem. While the process model is linear, the controller is nonlinear because of the inequality constraints. The objective of this research is to extend this theory to develop a more flexible and industrially relevant framework for process control applications. The issue of how to extend existing theory to the case of incomplete state measurements from nonlinear, stochastic systems will be investigated. This extension is important for industrial applications because the number and types of available sensors are limited and the sensors are subject to noise and occasional failure. This combined constrained state estimation and state feedback control, subject to the process constraints, would allow greater flexibility in designing the controller for challenging process control applications. The application of the method to nonlinear processes will also be explored, as most chemical processes are inherently nonlinear. This is an industry/university co operative research project; DuPont is cosponsoring the research. ! L !This program cannot be run in DOS mode. $ NE 4 =E V H @ P | V B P a ^ 0 ` 0 moire WEP KERNEL GDI USER ? ? ? c ? ? ? ? x ? moire1.exe SAVERDLGPROC SAVERINIT LAbstract - Rawlings - 9311420 Of the advanced process control methods, model-predictive control (MPC) is probably the most 0 0 ! ! D 0 0 ( Times New Roman Symbol & Arial " h k{ { { ~ a / Maria K. Burka Maria K. Burka