Robust Regulatory and Economic Predictive Control of Chemical Process Systems

模型预测控制(MPC)自三十年前被引入以来，迅速成为化工过程工业中的先进控制方法，并被广泛应用于各个工业部门。经济绩效目标的使用已经引起了所谓的经济MPC(EMPC)算法，其可以作为单级控制算法或者以级联方式应用，其中EMPC向内部监管MPC系统提供设置点。MPC依靠一个动态模型来预测未来的过程行为，以计算控制输入。没有一个模型是完美的，根据不确定性的程度，如果在控制计算中不考虑不确定性，特别是在违反工艺约束的情况下，可能会导致较差的控制性能。这导致在过去几年中制定了几项强有力的货币政策控制战略。然而，这些方法在很大程度上并没有严格地捕捉到假设不确定性下过程的闭环演化。

Model predictive control (MPC) rapidly became the advanced control method of choice in the chemical process industry after its introduction about three decades ago, and has since been widely adopted across a wide range of industrial sectors. The use of an economic performance objective has given rise to so-called economic MPC (EMPC) algorithms that may be applied either as a single-level control algorithm, or in a cascade manner with EMPC providing set-points to an inner regulatory MPC system. MPC relies on a dynamic model for prediction of future process behavior in order to calculate the control input. No model is perfect, and depending on the level of uncertainty, poor control performance can result if the uncertainty is not accounted for in the control calculation, particularly with regard to process constraint violations. This has led to the development of several robust MPC strategies over the past several years. However, these methods for the most part do not rigorously capture the closed-loop evolution of the process under the assumed uncertainty.