An Economic Model Predictive Control Framework for Smart Manufacturing

背景：过程系统是一个复杂的系统，涉及耦合的材料，能量和信息流。过程系统的有效运行包括许多不同的任务。根据执行每个任务的相对频率，任务通常被分类为以下分层功能层：计划、调度、实时优化（RTO）（其中进行经济优化以找到最佳操作条件），先进控制（其中模型预测控制（MPC）通常用于驱动过程以遵循由RTO层确定的操作条件）和调节控制。虽然这种不同任务的分层分离极大地简化了任务的设计和实现，并且在工业中取得了成功，但它也分离了分层结构中每个层可用的数据，并且不适合下一代智能制造范式（或工业4.0）。在智能制造模式中，数据将在不同任务之间无缝共享，决策将以分布式和协作的方式进行。近年来，经济MPC已经成为一种非常有前途的先进控制（决策）框架，它消除了RTO和先进控制之间的分离，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的核心技术之一。 建议研究：经济MPC的发展仍处于早期阶段，有许多问题需要解决。基于我们以前在经济MPC方面的工作，在这项拟议的研究中，我们将继续我们的长期目标，为智能制造开发一个灵活的经济模型预测控制框架。具体而言，在建议的工作中，我们会考虑以下重要事项：（经济优化和控制）在经济MPC通过区域跟踪;（B）计算效率高的算法，控制不变集近似;（c）发展一种有效的方法，（d）开发分布式经济MPC框架，该框架提供有保证的经济和控制性能;（e）将已开发的经济MPC框架应用于不同的过程系统，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：这项研究的成功将导致一个灵活的经济MPC框架与实用的性能评估方法。与此同时，一些HQP将接受培训，获得将经济MPC框架应用于不同系统的重要第一手经验。经济MPC框架和HQP将有助于加拿大制造业向智能制造范式的过渡。

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.