Robust Regulatory and Economic Predictive Control of Chemical Process Systems

化学过程系统的稳健监管和经济预测控制

• 批准号: RGPIN-2017-05627
• 负责人: Swartz, Christopher
• 金额: $ 2.04万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=637844
• 关键词: Robust; Regulatory; Economic; Predictive; Control

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.

模型预测控制（MPC）在大约三十年前引入后迅速成为化学过程工业中的先进控制方法的选择，并且已经在广泛的工业部门中被广泛采用。经济性能目标的使用已经产生了所谓的经济MPC（EMPC）算法，其可以作为单级控制算法应用，或者以级联方式应用，其中EMPC向内部调节MPC系统提供设定点。MPC依赖于用于预测未来过程行为的动态模型，以便计算控制输入。没有一个模型是完美的，并且取决于不确定性的水平，如果在控制计算中不考虑不确定性，特别是关于过程约束违反，则会导致不良的控制性能。这导致在过去几年中开发了几种强大的MPC策略。然而，这些方法在大多数情况下不严格捕获假设的不确定性下的过程的闭环演化。