GOALI: Turnkey Model Predictive Control: automated design, model identification, tuning, and monitoring

点击翻译按钮获取中文摘要

The goal of this research is to develop an integrated framework for design, model identification, tuning, and monitoring of industrial model-based control systems. New techniques will be developed to identify models from process measurements that enable practitioners to tune the control system to the application of interest. The proposed methodology is termed turnkey because process identification experiments and subsequent control system tuning parameter calculations will be automated. An added benefit of the turnkey approach is to remove the large variability introduced in current control system vendor products that require user experience and simulation studies to select these tuning parameters. The proposed control technology can be readily monitored to detect changes in the disturbances to the process and suggest intervention strategies. This automated monitoring of the control system is absent in industrial approaches in use today, providing a significant opportunity for improved business performance across many industrial sectors. In collaboration with the project’s industrial partner, Eastman Chemical, the proposed approach will be demonstrated on a full-scale, commercial, industrial chemical process. The approach also will be demonstrated in inexpensive university control laboratory experiments so that undergraduate students can be exposed to state-of-the-art control systems.The intuitive notion of online, repeated optimization of a model-based forecast as a means to design an automatic feedback control system has now taken hold in most advanced control technologies applied in the chemical process industries (e.g., model predictive control - MPC) as well as many other industrial sectors that include robotic motion control, flight autopilot systems, and land vehicle guidance control. Since the difficult and time-consuming element of controller deployment is obtaining models, the intellectual merit of the proposed research is to advance the state of the art in identifying linear actuator-to-sensor models plus the integrating disturbance models required for MPC. This combination of models is required in essentially all industrial applications, but no integrated theory is available for this task. Providing a turnkey system to move directly from data to values for all tuning parameters and demonstrating its performance on a challenging industrial process will enhance both the underlying fundamental control theory as well as the transfer of this technology to complex industrial manufacturing facilities. Although targeted to both traditional and new classes of chemical process control applications, the modeling, design, and monitoring methods developed in this research are sufficiently general to be applied to automated manufacturing problems arising in any manufacturing facility having production targets and constraints on materials, workflows, and inventories.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.