An Economic Model Predictive Control Framework for Smart Manufacturing

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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.