Robust Regulatory and Economic Predictive Control of Chemical Process Systems

模型预测控制（MPC）在大约三十年前推出后迅速成为化学过程工业的首选先进控制方法，并已被广泛应用于各种工业部门。经济绩效目标的使用产生了所谓的经济MPC （EMPC）算法，这种算法既可以作为单级控制算法应用，也可以以级联方式应用，EMPC为内部监管MPC系统提供设定值。MPC依赖于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，并且取决于不确定性的程度，如果在控制计算中没有考虑不确定性，特别是关于过程约束的违反，则可能导致较差的控制性能。在过去几年中，这导致了几个强有力的MPC战略的发展。然而，这些方法在很大程度上不能严格地捕捉过程在假定不确定性下的闭环演化。

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.