An Economic Model Predictive Control Framework for Smart Manufacturing

智能制造的经济模型预测控制框架

• 批准号: RGPIN-2019-04594
• 负责人: Liu, Jinfeng
• 金额: $ 2.04万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=715056
• 关键词: Economic; Model; Predictive; Control; Framework

Background: Process systems are complex systems that involve coupled materials, energy and information flows. The efficient operation of process systems includes many different tasks. According to the relative frequency with which each task is performed, the tasks are typically classified into the following hierarchical functioning layers: planning, scheduling, real-time optimisation (RTO) (in which economic optimisation is performed to find the best operating conditions), advanced control (in which model predictive control (MPC) is typically used to drive the process to follow the operating conditions determined by the RTO layer), and regulatory control. While this hierarchical separation of different tasks greatly simplifies the design and implementation of the tasks and has been successful in industry, it also separates data available to each layer in the hierarchy and is not suitable for the next generation smart manufacturing paradigm (or industry 4.0). In the smart manufacturing paradigm, data will be shared seamlessly between different tasks and decisions will be make in a distributed and cooperative manner. In recent years, economic MPC has emerged as a very promising advanced control (decision making) framework that removes the separation between RTO and advanced control, and performs economic optimisation and control in a unified framework. It has been recognised as one of the core techniques in smart manufacturing. Proposed research: The development of economic MPC is still in its early stage and there are many issues that need to be addressed. Based upon our previous work on economic MPC, in this proposed research, we will continue our long term goal in the development of a flexible economic model predictive control framework for smart manufacturing. Specifically, in the proposed work, we will consider the following important issues: (a) handling multiple objectives (economic optimization and control) in economic MPC through zone tracking; (b) computationally efficient algorithms for control invariant set approximation; (c) the development of an effective approach to characterise the (transient and infinite time) performance of economic MPC; (d) the development of a distributed economic MPC framework that gives guaranteed economic and control performance; (e) the applications of developed economic MPC framework to different process systems including oil sands processing processes, wastewater treatment plants, biomedical systems, irrigation processes. Impacts: The success of this proposed research will results in a flexible economic MPC framework with practical performance assessing methods. At the same time, a few HQP will be trained with important firsthand experience in applying the economic MPC framework to different systems. The economic MPC framework and the HQP will contribute to the transition of Canadian's manufacturing industries to the smart manufacturing paradigm.

背景：过程系统是涉及材料，能量和信息流耦合的复杂系统。过程系统的有效操作包括许多不同的任务。根据执行每个任务的相对频率，通常将任务分类为以下层次结构功能层：计划，调度，实时优化（RTO）（在其中执行经济优化以找到最佳的操作条件），高级控制（该模型预测性控制（MPC）通常用于遵循操作条件，以通过RTO层确定操作条件），并通过RTO层来确定RTO层）。尽管这种不同任务的层次结构分离极大地简化了任务的设计和实施，并且在行业中取得了成功，但它也将可用的数据分开到层次结构中的每个层，并且不适合下一代智能制造范式（或行业4.0）。在智能制造范式中，将以分布式和合作的方式在不同任务和决策之间无缝共享数据。近年来，经济MPC已成为一个非常有前途的高级控制（决策）框架，它消除了RTO和高级控制之间的分离，并在统一的框架中执行经济优化和控制。它被认为是智能制造中的核心技术之一。 拟议的研究：经济MPC的发展仍处于早期阶段，需要解决许多问题。基于我们先前关于经济MPC的工作，在这项拟议的研究中，我们将继续我们的长期目标，以开发灵活的经济模型预测智能制造框架。具体而言，在拟议的工作中，我们将考虑以下重要问题：（a）通过区域跟踪处理经济MPC中的多个目标（经济优化和控制）； （b）用于控制不变设置近似的计算有效算法； （c）开发一种有效的方法来表征经济MPC的（瞬时和无限时间）的表现； （d）开发分布式经济MPC框架，该框架提供了保证的经济和控制绩效； （e）开发的经济MPC框架在不同过程系统中的应用，包括油砂加工过程，废水处理厂，生物医学系统，灌溉过程。 影响：这项拟议的研究的成功将带来一个灵活的经济MPC框架，并采用实际的绩效评估方法。同时，将在将经济MPC框架应用于不同系统的情况下，对一些重要的第一手经验进行一些HQP的培训。经济MPC框架和HQP将有助于加拿大制造业向智能制造范式过渡。