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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.

这项研究的目标是为基于工业模型的控制系统的设计、模型识别、整定和监控开发一个集成的框架。将开发新的技术来从过程测量中识别模型，使从业者能够调整控制系统以适应感兴趣的应用。建议的方法被称为交钥匙，因为过程识别实验和随后的控制系统调整参数计算将自动进行。交钥匙方法的另一个好处是消除了当前控制系统供应商产品中引入的巨大变异性，这些产品需要用户体验和模拟研究来选择这些调整参数。建议的控制技术可以很容易地被监测，以检测对过程的扰动的变化，并建议干预策略。控制系统的这种自动化监控在当今使用的工业方法中是不存在的，这为改善许多工业部门的业务绩效提供了一个重要的机会。在与该项目的工业合作伙伴伊士曼化学公司的合作下，拟议的方法将在一个全面的、商业的、工业的化学过程中进行演示。这种方法还将在廉价的大学控制实验室实验中得到演示，以便本科生能够接触到最先进的控制系统。在线、重复优化基于模型的预测作为设计自动反馈控制系统的一种手段的直观概念现在已在化学过程工业中应用的最先进控制技术(例如，模型预测控制-MPC)以及包括机器人运动控制、飞行自动驾驶系统和陆地车辆制导控制在内的许多其他工业部门中站稳脚跟。由于控制器部署的困难和耗时的因素是获得模型，所提出的研究的智能优点是在识别预测控制所需的线性执行器-传感器模型和积分扰动模型方面取得了进展。这种模型的组合在基本上所有的工业应用中都是需要的，但没有完整的理论可用于这项任务。提供一个可直接从数据转换为所有调整参数的值的交钥匙系统，并展示其在具有挑战性的工业过程中的性能，将增强基本控制理论以及将这项技术转移到复杂的工业制造设施。虽然针对传统和新型的化学过程控制应用，本研究中开发的建模、设计和监控方法足够通用，适用于具有生产目标和对材料、工作流程和库存的限制的任何制造设施中出现的自动化制造问题。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。