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

GOALI：交钥匙模型预测控制：自动化设计、模型识别、调整和监控

基本信息

批准号：
2138985
负责人：
James Rawlings
金额：
$ 31.45万
依托单位：
University of California-Santa Barbara
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-15 至 2025-07-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2138985&HistoricalAwards=false
关键词：
GOALI Turnkey Model Predictive Control

项目摘要

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.

这项研究的目的是为基于工业模型的控制系统的设计，模型识别，调整和监视开发一个集成框架。将开发新技术来从过程测量中识别模型，使实践者能够将控制系统调整为利益的应用。所提出的方法称为交钥匙，因为过程识别实验和随后的控制系统调谐参数计算将被自动化。交钥匙方法的另一个好处是，要删除需要用户体验和仿真研究选择这些调谐参数的当前控制系统供应商产品中引入的大变异性。可以轻松监控所提出的控制技术，以检测灾难对过程的变化并提出干预策略。当今使用的工业方法吸收了对控制系统的自动监测，为改善许多工业领域的业务绩效提供了重要的机会。与该项目的工业合作伙伴伊士曼化学（Eastman Chemical）合作，将在全面的商业，工业化学过程中证明拟议的方法。该方法还将在廉价的大学控制实验室实验中进行证明，以便可以接触到本科生到最先进的控制系统。在线的直觉概念，重复对基于模型的预测作为设计自动反馈控制系统的手段，现在已经在化学工艺工业中应用了许多高级控制技术（例如，MADED）（例如，MADC） - MPC-MPC。控制，飞行自动驾驶系统和陆地车辆指导控制。由于控制器部署的困难且耗时的要素是获得模型，因此拟议研究的智力优点是促进最新技术的状态，以识别线性执行器到传感器模型以及MPC所需的集成灾难模型。基本上所有工业应用中都需要这种模型的组合，但是对于此任务没有综合理论。提供一个交钥匙系统，以直接从数据转移到所有调整参数的值，并在挑战工业过程中展示其表现将增强基本基本控制理论以及将该技术转移到复杂的工业制造设施。尽管针对传统和新的化学过程控制应用类别，但本研究中开发的建模，设计和监视方法足以将其应用于具有生产目标和限制的材料，工作流程和库存的生产目标和限制的任何制造设施中引起的自动化制造问题。这对NSF的法定任务和审查的审查均通过评估来弥补，这表明了良好的范围，这是通过评估的支持。